local dont_be_silly = false

local ook_start_x = 10

local gui = require 'gui'

local dialog = require 'gui.dialogs'

local widgets = require 'gui.widgets'

local guiScript = require 'gui.script'

local utils = require 'utils'

local Ui

local translateName = dfhack.TranslateName or dfhack.translation.translateName

local DF_pre_50 = (tonumber(string.match(dfhack.getDFVersion(), "^v?(%d+%.%d+)")) < 0.50)

local Cursor_Left = (DF_pre_50) and "CURSOR_LEFT" or "KEYBOARD_CURSOR_LEFT"

local Cursor_Right = (DF_pre_50) and "CURSOR_RIGHT" or "KEYBOARD_CURSOR_RIGHT"

local values = {[df.value_type.LAW] =

{[-3] = "finds the idea of law abhorrent",

[-2] = "disdains the law",

[-1] = "does not respect the law",

[0] = "doesn't feel strongly about the law",

[1] = "respects the law",

[2] = "has a great deal of respect for the law",

[3] = "is an absolute believer in the rule of law"},

[df.value_type.LOYALTY] =

{[-3] = "is disgusted by the idea of loyalty",

[-2] = "disdains loyalty",

[-1] = "views loyalty unfavorably",

[0] = "doesn't particularly value loyalty",

[1] = "values loyalty",

[2] = "greatly prizes loyalty",

[3] = "has the highest regard for loyalty"},

[df.value_type.FAMILY] =

{[-3] = "finds the idea of family loathsome",

[-2] = "lacks any respect for family",

[-1] = "is put off by family",

[0] = "does not care about family one way or the other",

[1] = "values family",

[2] = "values family greatly",

[3] = "sees family as one of the most important things in life"},

[df.value_type.FRIENDSHIP] =

{[-3] = "finds the whole idea of friendship disgusting",

[-2] = "is completely put off by the idea of friends",

[-1] = "finds friendship burdensome",

[0] = "does not care about friendship",

[1] = "thinks friendship is important",

[2] = "sees friendship as one of the finer things in life",

[3] = "believes friendship is the key to the ideal life"},

[df.value_type.POWER] =

{[-3] = "finds the acquisition and use of power abhorrent and would have all masters toppled",

[-2] = "hates those who wield power over others",

[-1] = "has a negative view of those who exercise power over others",

[0] = "doesn't find power particularly praiseworthy",

[1] = "respects power",

[2] = "sees power over others as something to strive for",

[3] = "believes that the acquisition of power over others is the ideal goal in life and worthy of the highest respect"},

[df.value_type.TRUTH] =

{[-3] = "is repelled by the idea of honesty and lies without compunction",

[-2] = "sees lying as an important means to an end",

[-1] = "finds blind honesty foolish",

[0] = "does not particularly value the truth",

[1] = "values honesty",

[2] = "believes that honesty is a high ideal",

[3] = "believes the truth is inviolable regardless of the cost"},

[df.value_type.CUNNING] =

{[-3] = "is utterly disgusted by guile and cunning",

[-2] = "holds shrewd and crafty individuals in the lowest esteem",

[-1] = "sees guile and cunning as indirect and somewhat worthless",

[0] = "does not really value cunning and guile",

[1] = "values cunning",

[2] = "greatly respects the shrewd and guileful",

[3] = "holds well-laid plans and shrewd deceptions in the highest regard"},

[df.value_type.ELOQUENCE] =

{[-3] = "sees artful speech and eloquence as a wasteful form of deliberate deception and treats it as such",

[-2] = "finds [him]self somewhat disgusted with eloquent speakers",

[-1] = "finds eloquence and artful speech off-putting",

[0] = "doesn't value eloquence so much",

[1] = "values eloquence",

[2] = "deeply respects eloquent speakers",

[3] = "believes that artful speech and eloquent expression are of the highest ideals"},

[df.value_type.FAIRNESS] =

{[-3] = "is disgusted by the idea of fairness and will freely cheat anybody at any time",

[-2] = "finds the idea of fair-dealing foolish and cheats whenever [he] finds it profitable",

[-1] = "sees life as unfair and doesn't mind it that way",

[0] = "does not care about fairness", -- one way or the other?

[1] = "respects fair-dealing and fair-play",

[2] = "has great respect for fairness",

[3] = "holds fairness as one of the highest ideals and despises cheating of any kind"},

[df.value_type.DECORUM] =

{[-3] = "is affronted of the whole notion of maintaining decorum and finds so-called dignified people disgusting",

[-2] = "sees those that attempt to maintain dignified and proper behavior as vain and offensive",

[-1] = "finds maintaining decorum a silly, fumbling waste of time",

[0] = "doesn't care very much about decorum",

[1] = "values decorum, dignity and proper behavior",

[2] = "greatly respects those that observe decorum and maintain their dignity",

[3] = "views decorum as a high ideal and is deeply offended by those that fail to maintain it"},

[df.value_type.TRADITION] =

{[-3] = "is disgusted by tradition and would flout any [he] encounters if given a chance",

[-2] = "find the following of tradition foolish and limiting",

[-1] = "disregards tradition",

[0] = "doesn't have any strong feelings about tradition",

[1] = "values tradition",

[2] = "is a firm believer in the value of tradition",

[3] = "holds the maintenance of tradition as one of the highest ideals"},

[df.value_type.ARTWORK] =

{[-3] = "finds art offensive and would have it destroyed whenever possible",

[-2] = "sees the whole pursuit of art as silly",

[-1] = "finds artwork boring",

[0] = "doesn't care about art one way or another",

[1] = "values artwork",

[2] = "greatly respects artists and their work",

[3] = "believes that the creation and appreciation of artwork is one of the highest ideals"},

[df.value_type.COOPERATION] =

{[-3] = "is thoroughly disgusted by cooperation",

[-2] = "views cooperation as a low ideal not worthy of any respect",

[-1] = "dislikes cooperation",

[0] = "doesn't see cooperation as valuable",

[1] = "values cooperation",

[2] = "sees cooperation as very important in life",

[3] = "places cooperation as one of the highest ideals"},

[df.value_type.INDEPENDENCE] =

{[-3] = "hates freedom and would crush the independent spirit wherever it is found",

[-2] = "sees freedom and independence as completely worthless",

[-1] = "finds the idea of independence and freedom somewhat foolish",

[0] = "doesn't really value independence one way or another",

[1] = "values independence",

[2] = "treasures independence",

[3] = "believes that freedom and independence are completely non-negotiable and would fight to defend them"},

[df.value_type.STOICISM] =

{[-3] = "sees concealment of emotions as a betrayal and tries [his] best never to associate with such secretive fools",

[-2] = "feels that those who attempt to conceal their emotions are vain and foolish",

[-1] = "sees no value in holding back complaints and concealing emotions",

[0] = "doesn't see much value in being stoic",

[1] = "believes it is important to conceal emotions and refrain from complaining",

[2] = "thinks it is of the utmost importance to present a bold face and never grouse, complain, and even show emotion",

[3] = "views any show of emotion as offensive"},

[df.value_type.INTROSPECTION] =

{[-3] = "finds the whole idea of introspection completely offensive and contrary to the ideals of a life well-lived",

[-2] = "thinks that introspection is valueless and those that waste time in self-examination are deluded fools",

[-1] = "finds introspection to be a waste of time",

[0] = "doesn't really see the value in self-examination",

[1] = "sees introspection as important",

[2] = "deeply values introspection",

[3] = "feels that introspection and all forms of self-examination are the keys to a good life and worthy of respect"},

[df.value_type.SELF_CONTROL] =

{[-3] = "has abandoned any attempt at self-control and finds the whole concept deeply offensive",

[-2] = "sees the denial of impulses as a vain and foolish pursuit",

[-1] = "finds those that deny their impulses somewhat stiff",

[0] = "doesn't particularly value self-control",

[1] = "values self-control",

[2] = "finds moderation and self-control to be very important",

[3] = "believes that self-mastery and the denial of impulses are of the highest ideals"},

[df.value_type.TRANQUILITY] =

{[-3] = "is disgusted by tranquility and would that the world would constantly churn with noise and activity",

[-2] = "is greatly disturbed by quiet and a peaceful existence",

[-1] = "prefers a noisy, bustling life to boring days without activity",

[0] = "doesn't have a preference between tranquility and tumult",

[1] = "values tranquility and a peaceful day",

[2] = "strongly values tranquility and quiet",

[3] = "views tranquility as one of the highest ideals"},

[df.value_type.HARMONY] =

{[-3] = "believes deeply that chaos and disorder are the truest expressions of life and would disrupt harmony wherever it is found",

[-2] = "can't fathom why anyone would want to live in an orderly and harmonious society",

[-1] = "doesn't respect a society that has settled into harmony without debate and strife",

[0] = "sees equal parts of harmony and discord as parts of life",

[1] = "values a harmonious existence",

[2] = "strongly believes that a peaceful and ordered society without dissent is best",

[3] = "would have the world operate in complete harmony without the least bit of strife and disorder"},

[df.value_type.MERRIMENT] =

{[-3] = "is appalled by merrymaking, parties and other such worthless activities",

[-2] = "is disgusted by merrymakers",

[-1] = "sees merrymaking as a waste",

[0] = "doesn't really value merrymaking",

[1] = "finds merrymaking and parying worthwhile activities",

[2] = "truly values merrymaking and parties",

[3] = "believes that little is better in life than a good party"},

[df.value_type.CRAFTSMANSHIP] =

{[-3] = "views craftdwarfship with disgust and would desecrate a so-called masterwork or two if [he] could get away with it",

[-2] = "sees the pursuit of good craftdwarfship as a total waste",

[-1] = "considers craftdwarfship to be relatively worthless",

[0] = "doesn't particularly care about crafdwarfship",

[1] = "values good craftdwarfship",

[2] = "has a great deal of respect for worthy craftdwarfship",

[3] = "holds craftdwarfship to be of the highest ideals and celebrates talented artisans and their masterworks"},

[df.value_type.MARTIAL_PROWESS] =

{[-3] = "abhors those who pursue the mastery of weapons and skill with fighting",

[-2] = "thinks that the pursuit of the skills of warfare and fighting is a low pursuit indeed",

[-1] = "finds those that develop skills with weapons and fighting distasteful",

[0] = "does not really value skills related to fighting",

[1] = "values martial prowess",

[2] = "deeply respects skill at arms",

[3] = "believes that martial prowess defines the good character of an individual"},

[df.value_type.SKILL] =

{[-3] = "sees the whole idea of taking time to master a skill as appalling",

[-2] = "believes that the time taken to master a skill is a horrible waste",

[-1] = "finds the pursuit of skill mastery off-putting",

[0] = "doesn't care if others take the time to master skills",

[1] = "respects the development of skill",

[2] = "really respects those that take the time to master a skill",

[3] = "believes that the mastery of a skill is one of the highest pursuits"},

[df.value_type.HARD_WORK] =

{[-3] = "finds the proposition that one should work hard in life utterly abhorrent",

[-2] = "thinks working hard is an abject idiocy",

[-1] = "sees working hard as a foolish waste of time",

[0] = "doesn't really see the point of working hard",

[1] = "values hard work",

[2] = "deeply respects those that work hard at their labors",

[3] = "believes that hard work is one of the highest ideals and a key to the good life"},

[df.value_type.SACRIFICE] =

{[-3] = "thinks that the whole concept of sacrifice for others is truly disgusting",

[-2] = "finds sacrifice to be the height of folly",

[-1] = "sees sacrifice as wasteful and foolish",

[0] = "doesn't particularly respect sacrifice as a virtue",

[1] = "values sacrifice",

[2] = "believes that those who sacrifice for others should be deeply respected",

[3] = "sacrifice to be one of the highest ideals"},

[df.value_type.COMPETITION] =

{[-3] = "finds the very idea of competition obscene",

[-2] = "deeply dislikes competition",

[-1] = "sees competition as wasteful and silly",

[0] = "doesn't have strong views on competition",

[1] = "sees competition as reasonably important",

[2] = "views competition as a crucial driving force of the world",

[3] = "holds the idea of competition among the most important and would encourage it whenever possible"},

[df.value_type.PERSEVERENCE] =

{[-3] = "finds the notion that one would persevere through adversity completely abhorrent",

[-2] = "thinks there is something deeply wrong with people the persevere through adversity",

[-1] = "sees perseverance in the face of adversity as bull-headed and foolish",

[0] = "doesn't think much about the idea of perseverance",

[1] = "respects perseverance",

[2] = "greatly respects individuals that persevere through their trials and labors",

[3] = "believes that perseverance is one of the greatest qualities somebody can have"},

[df.value_type.LEISURE_TIME] =

{[-3] = "believes that those that take leisure time are evil and finds the whole idea disgusting",

[-2] = "is offended by leisure time and leisurely living",

[-1] = "finds leisure time wasteful", -- also "prefers a noisy, bustling life to boring days without activity",?

[0] = "doesn't think one way or the other about leisure time",

[1] = "values leisure time",

[2] = "treasures leisure time and thinks it is very important in life",

[3] = "believes it would be a fine thing if all time were leisure time"},

[df.value_type.COMMERCE] =

{[-3] = "holds the view that commerce is a vile obscenity",

[-2] = "finds those that engage in trade and commerce to be fairly disgusting",

[-1] = "is somewhat put off by trade and commerce",

[0] = "doesn't particularly respect commerce",

[1] = "respects commerce",

[2] = "really respects commerce and those that engage in trade",

[3] = "sees engaging in commerce as a high ideal in life"},

[df.value_type.ROMANCE] =

{[-3] = "finds even the abstract idea of romance repellent",

[-2] = "is somewhat disgusted by romance",

[-1] = "finds romance distasteful",

[0] = "doesn't care one way or the other about romance",

[1] = "values romance",

[2] = "thinks romance is very important in life",

[3] = "sees romance as one of the highest ideals"},

[df.value_type.NATURE] =

{[-3] = "would just as soon have nature and the great outdoors burned to ashes and converted into a great mining pit",

[-2] = "has a deep dislike for the natural world",

[-1] = "finds nature somewhat disturbing",

[0] = "doesn't care about nature one way or another",

[1] = "values nature",

[2] = "has a deep respect for animals, plants and the natural world",

[3] = "holds nature to be of greater value than most aspects of civilization"},

[df.value_type.PEACE] =

{[-3] = "thinks that the world should be engaged into perpetual warfare",

[-2] = "believes war is preferable to peace in general",

[-1] = "sees was as a useful means to an end",

[0] = "doesn't particularly care between war and peace",

[1] = "values peace over war",

[2] = "believes that peace is always preferable to war",

[3] = "believes that the idea of war is utterly repellent and would have peace at all costs"},

[df.value_type.KNOWLEDGE] =

{[-3] = "sees the attainment and preservation of knowledge as an offensive enterprise engaged in by arrogant fools",

[-2] = "thinks the quest for knowledge is a delusional fantasy",

[-1] = "finds the pursuit of knowledge to be a waste of effort",

[0] = "doesn't see the attainment of knowledge as important",

[1] = "values knowledge",

[2] = "views the pursuit of knowledge as deeply important",

[3] = "finds the quest for knowledge to be of the very highest value"}}

local knowledge =

{[0] = {[df.knowledge_scholar_flags_0.philosophy_logic_formal_reasoning] = "Philosophy, Logic, Formal reasoning",

[df.knowledge_scholar_flags_0.philosophy_logic_deductive_reasoning] = "Philosophy, Logic, Deductive reasoning",

[df.knowledge_scholar_flags_0.philosophy_logic_syllogistic_logic] = "Philosophy, Logic, Syllogistic logic",

[df.knowledge_scholar_flags_0.philosophy_logic_hypothetical_syllogisms] = "Philosophy, Logic, Hypothetical syllogisms",

[df.knowledge_scholar_flags_0.philosophy_logic_propositional_logic] = "Philosophy, Logic, Propositional",

[df.knowledge_scholar_flags_0.philosophy_logic_dialectic_reasoning] = "Philosophy, Logic, Dialectic reasoning",

[df.knowledge_scholar_flags_0.philosophy_logic_analogical_inference] = "Philosophy, Logic, Analogical inference",

[df.knowledge_scholar_flags_0.philosophy_ethics_applied_medical] = "Philosophy, Ethics, Medical",

[df.knowledge_scholar_flags_0.philosophy_ethics_individual_value] = "Philosophy, Ethics, Individual value",

[df.knowledge_scholar_flags_0.philosophy_ethics_state_consequentialism] = "Philosophy, Ethics, State consequentialism",

[df.knowledge_scholar_flags_0.philosophy_epistemology_truth] = "Philosophy, Epistemology, Truth",

[df.knowledge_scholar_flags_0.philosophy_epistemology_perception] = "Philosophy, Epistemology, Perception",

[df.knowledge_scholar_flags_0.philosophy_epistemology_justification] = "Philosophy, Epistemology, Justification",

[df.knowledge_scholar_flags_0.philosophy_epistemology_belief] = "Philosophy, Epistemology, Belief",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_existence] = "Philosophy, Metaphysics, Existence",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_time] = "Philosophy, Metaphysics, Time",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_mind_body] = "Philosophy, Metaphysics, Mind-body",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_objects_and_properties] = "Philosophy, Metaphysics, Objects and properties",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_wholes_and_parts] = "Philosophy, Metaphysics, Wholes and parts",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_events] = "Philosophy, Metaphysics, Events",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_processes] = "Philosophy, Metaphysics, Processes",

[df.knowledge_scholar_flags_0.philosophy_metaphysics_causation] = "Philosophy, Metaphysics, Causation",

[df.knowledge_scholar_flags_0.philosophy_ethics_applied_military] = "Philosophy, Ethics applied, Military",

[df.knowledge_scholar_flags_0.philosophy_ethics_applied_interpersonal_conduct] = "Philosophy, Ethics applied, Interpersonal ethics",

[df.knowledge_scholar_flags_0.philosophy_specialized_law] = "Philosophy, Specialized, Law",

[df.knowledge_scholar_flags_0.philosophy_specialized_education] = "Philosophy, Specialized, Education",

[df.knowledge_scholar_flags_0.philosophy_specialized_language_grammar] = "Philosophy, Specialized language, Grammar",

[df.knowledge_scholar_flags_0.philosophy_specialized_language_etymology] = "Philosophy, Specialized language Etymology",

[df.knowledge_scholar_flags_0.philosophy_specialized_politics_diplomacy] = "Philosophy, Specialized politics, Diplomacy",

[df.knowledge_scholar_flags_0.philosophy_specialized_politics_government_forms] = "Philosophy, Specialized politics, Government forms",

[df.knowledge_scholar_flags_0.philosophy_specialized_politics_economic_policy] = "Philosophy, Specialized politics, Economic policy",

[df.knowledge_scholar_flags_0.philosophy_specialized_politics_social_welfare] = "Philosophy, Specialized politics, Social welfare"},

[1] = {[df.knowledge_scholar_flags_1.philosophy_logic_inductive_reasoning] = "Philosophy, Logic, Inductive reasoning",

[df.knowledge_scholar_flags_1.philosophy_logic_direct_inference] = "Philosophy, Logic, Direct inference",

[df.knowledge_scholar_flags_1.philosophy_aesthetics_nature_of_beauty] = "Philosophy, Aesthetics, Nature of beauty",

[df.knowledge_scholar_flags_1.philosophy_aesthetics_value_of_art] = "Philosophy, Aesthetics, Value of art",

[df.knowledge_scholar_flags_1.philosophy_specialized_language_dictionary] = "Philosophy, Specialized language, Dictionary"},

[2] = {[df.knowledge_scholar_flags_2.mathematics_method_proof_by_contradiction] = "Mathematics, Method, Proof by contradiction",

[df.knowledge_scholar_flags_2.mathematics_notation_zero] = "Mathematics, Notation, Zero",

[df.knowledge_scholar_flags_2.mathematics_notation_negative_numbers] = "Mathematics, Notation, Negative numbers",

[df.knowledge_scholar_flags_2.mathematics_notation_large_numbers] = "Mathematics, Notation, Scientific notation",

[df.knowledge_scholar_flags_2.mathematics_notation_positional] = "Mathematics, Notation, Positional",

[df.knowledge_scholar_flags_2.mathematics_geometry_basic_objects] = "Mathematics, Geometry, Basic objects",

[df.knowledge_scholar_flags_2.mathematics_method_exhaustion] = "Mathematics, Method, Exhaustion",

[df.knowledge_scholar_flags_2.mathematics_geometry_similar_and_congruent_triangles] = "Mathematics, Geometry, Similar and congruent triangles",

[df.knowledge_scholar_flags_2.mathematics_geometry_geometric_mean_theorem] = "Mathematics, Geometry, Geometric mean theorem",

[df.knowledge_scholar_flags_2.mathematics_geometry_isosceles_base_angles_equal] = "Mathematics, Geometry, Isosceles base angles equal",

[df.knowledge_scholar_flags_2.mathematics_geometry_inscribed_triangle_on_diameter_is_right] = "Mathematics, Geometry, Inscribed triangle on diameter is right",

[df.knowledge_scholar_flags_2.mathematics_geometry_pythagorean_theorem] = "Mathematics, Geometry, Pythagorean theorem",

[df.knowledge_scholar_flags_2.mathematics_geometry_pythagorean_triples_small] = "Mathematics, Geometry, Pythagorean triples small",

[df.knowledge_scholar_flags_2.mathematics_geometry_pythagorean_triples_3_digit] = "Mathematics, Geometry, Pythagorean triples 3 digit",

[df.knowledge_scholar_flags_2.mathematics_geometry_pythagorean_triples_4_digit] = "Mathematics, Geometry, Pythagorean triples 4 digit",

[df.knowledge_scholar_flags_2.mathematics_geometry_existence_of_incommensurable_ratios] = "Mathematics, Geometry, Irrational numbers",

[df.knowledge_scholar_flags_2.mathematics_method_axiomatic_reasoning] = "Mathematics, Method, Axiomatic reasoning",

[df.knowledge_scholar_flags_2.mathematics_numbers_unique_prime_factorization] = "Mathematics, Numbers, Unique prime factorization",

[df.knowledge_scholar_flags_2.mathematics_numbers_algorithm_for_computing_gcd] = "Mathematics, Numbers, Algorithm for computing GCD",

[df.knowledge_scholar_flags_2.mathematics_geometry_volume_of_pyramid] = "Mathematics, Geometry, Volume of pyramid",

[df.knowledge_scholar_flags_2.mathematics_geometry_volume_of_cone] = "Mathematics, Geometry, Volume of cone",

[df.knowledge_scholar_flags_2.mathematics_geometry_volume_of_sphere] = "Mathematics, Geometry, Volume of Sphere",

[df.knowledge_scholar_flags_2.mathematics_geometry_pi_to_4_digits] = "Mathematics, Geometry, Pi to 4 digits",

[df.knowledge_scholar_flags_2.mathematics_numbers_division_algorithm] = "Mathematics, Numbers, Division algorithm",

[df.knowledge_scholar_flags_2.mathematics_geometry_table_of_chord_values] = "Mathematics, Geometry, Chord tables",

[df.knowledge_scholar_flags_2.mathematics_geometry_area_of_triangle_from_side_lengths] = "Mathematics, Geometry, Area of triangle from side lengths",

[df.knowledge_scholar_flags_2.mathematics_geometry_area_of_circle] = "Mathematics, Geometry, Area of circle",

[df.knowledge_scholar_flags_2.mathematics_geometry_pi_to_6_digits] = "Mathematics, Geometry, Pi to 6 digits",

[df.knowledge_scholar_flags_2.mathematics_geometry_definitions_and_basic_properties_of_conic_sections] = "Mathematics, Geometry, Conic sections",

[df.knowledge_scholar_flags_2.mathematics_numbers_chinese_remainder_algorithm] = "Mathematics, Numbers, Chinese remainder algorithm",

[df.knowledge_scholar_flags_2.mathematics_geometry_area_enclosed_by_line_and_parabola] = "Mathematics, Geometry, Area enclosed by line and parabola",

[df.knowledge_scholar_flags_2.mathematics_numbers_sieve_algorithm_for_primes] = "Mathematics, Numbers, Sieve algorithm for primes"},

[3] = {[df.knowledge_scholar_flags_3.mathematics_numbers_root_2_to_5_digits] = "Mathematics, Numbers, Approximation of root 2",

[df.knowledge_scholar_flags_3.mathematics_numbers_infinite_primes] = "Mathematics, Numbers, Euclid's Theorem",

[df.knowledge_scholar_flags_3.mathematics_numbers_root_2_irrational] = "Mathematics, Numbers Irrationality of root 2",

[df.knowledge_scholar_flags_3.mathematics_geometry_surface_area_of_sphere] = "Mathematics, Gometry, Surface area of sphere",

[df.knowledge_scholar_flags_3.mathematics_algebra_finite_summation_formulas] = "Mathematics, Algebra, Large sums",

[df.knowledge_scholar_flags_3.mathematics_algebra_solving_linear_systems] = "Mathematics, Algebra, Systems of equations",

[df.knowledge_scholar_flags_3.mathematics_algebra_balancing_and_completion] = "Mathematics, Algbra, Balancing and completion",

[df.knowledge_scholar_flags_3.mathematics_algebra_quadratic_by_completing_square] = "Mathematics, Algebra, Quadratic by completing square",

[df.knowledge_scholar_flags_3.mathematics_algebra_quadratic_formula] = "Mathematics, Algebra, Quadratic formula",

[df.knowledge_scholar_flags_3.mathematics_notation_syncopated_algebra] = "Mathematics, Notation, Syncopated algebra",

[df.knowledge_scholar_flags_3.mathematics_geometry_law_of_sines] = "Mathematics, Geometry, Law of sines",

[df.knowledge_scholar_flags_3.mathematics_geometry_angle_sum_difference_trig_identities] = "Mathematics, Geometry, Sum-difference trig identities",

[df.knowledge_scholar_flags_3.mathematics_algebra_pascals_triangle] = "Mathematics, Algebra, Pascal's triangle",

[df.knowledge_scholar_flags_3.mathematics_algebra_solving_higher_order_polynomials] = "Mathematics, Algebra, Solving higher order polynomials",

[df.knowledge_scholar_flags_3.mathematics_notation_early_symbols_for_operations] = "Mathematics, Notation, Symbol for addition",

[df.knowledge_scholar_flags_3.mathematics_algebra_divergence_of_harmonic_series] = "Mathematics, Algebra, Divergence of harmonic series",

[df.knowledge_scholar_flags_3.mathematics_geometry_properties_of_chords] = "Mathematics, Geometry, Properties of chords"},

[4] = {[df.knowledge_scholar_flags_4.history_sourcing_basic_reliability] = "History, Sourcing, Source reliability",

[df.knowledge_scholar_flags_4.history_sourcing_role_of_systemic_bias] = "History, Sourcing, Role of systemic bias",

[df.knowledge_scholar_flags_4.history_sourcing_role_of_state_bias_and_propaganda] = "History, Sourcing, Role of state bias and propaganda",

[df.knowledge_scholar_flags_4.history_sourcing_personal_interviews] = "History, Sourcing, Personal interviews",

[df.knowledge_scholar_flags_4.history_theory_historical_causation] = "History, Theory, Historical causation",

[df.knowledge_scholar_flags_4.history_theory_historical_cycles] = "History, Theory, Historical cycles",

[df.knowledge_scholar_flags_4.history_theory_social_cohesion] = "History, Theory, Sociology",

[df.knowledge_scholar_flags_4.history_theory_social_conflict] = "History, Theory, Social conflict",

[df.knowledge_scholar_flags_4.history_form_biography] = "History, Form, Biography",

[df.knowledge_scholar_flags_4.history_form_comparative_biography] = "History, Form, Comparative biography",

[df.knowledge_scholar_flags_4.history_form_biographical_dictionaries] = "History, Form, Biographical dictionaries",

[df.knowledge_scholar_flags_4.history_form_autobiographical_adventure] = "History, Form, Autobiographical adventure",

[df.knowledge_scholar_flags_4.history_form_genealogy] = "History, Form, Genealogy",

[df.knowledge_scholar_flags_4.history_form_encyclopedia] = "History, Form, Encyclopedia",

[df.knowledge_scholar_flags_4.history_form_cultural_history] = "History, Form, Cultural history",

[df.knowledge_scholar_flags_4.history_form_cultural_comparison] = "History, Form, Comparative anthropology",

[df.knowledge_scholar_flags_4.history_sourcing_role_of_cultural_differences] = "History, Sourcing, Role of cultural differences",

[df.knowledge_scholar_flags_4.history_form_alternate_history] = "History, Form, Alternate history",

[df.knowledge_scholar_flags_4.history_sourcing_basic_archaeology] = "History, Sourcing, Archaeology",

[df.knowledge_scholar_flags_4.history_form_treatise_on_tech_evolution] = "History, Form, Treatise on tech evolution"},

[5] = {[df.knowledge_scholar_flags_5.astronomy_phases_of_the_moon] = "Astronomy, -, Phases of the moon",

[df.knowledge_scholar_flags_5.astronomy_summer_winter_moon] = "Astronomy, -, Summer winter moon",

[df.knowledge_scholar_flags_5.astronomy_path_of_the_moon] = "Astronomy, -, Path of the moon",

[df.knowledge_scholar_flags_5.astronomy_tides_and_the_moon] = "Astronomy, -, Tides and the moon",

[df.knowledge_scholar_flags_5.astronomy_height_of_tides_vs_moon_and_sun] = "Astronomy, -, Tides and the moon",

[df.knowledge_scholar_flags_5.astronomy_summer_winter_sun] = "Astronomy, -, Summer winter sun",

[df.knowledge_scholar_flags_5.astronomy_relationship_between_lunar_solar_year] = "Astronomy, -, Relationship between lunar solar year",

[df.knowledge_scholar_flags_5.astronomy_daylight_variation_with_solar_year] = "Astronomy, -, Daylight variation with solar year",

[df.knowledge_scholar_flags_5.astronomy_geocentric_model] = "Astronomy, -, Geocentric model",

[df.knowledge_scholar_flags_5.astronomy_heliocentric_model] = "Astronomy, -, Heliocentric model",

[df.knowledge_scholar_flags_5.astronomy_dates_of_lunar_and_solar_eclipses] = "Astronomy, -, Dates of lunar and solar eclipses",

[df.knowledge_scholar_flags_5.astronomy_star_charts] = "Astronomy, -, Astrography",

[df.knowledge_scholar_flags_5.astronomy_star_catalogues_100] = "Astronomy, -, Star catalogues 100",

[df.knowledge_scholar_flags_5.astronomy_star_catalogues_1000] = "Astronomy, -, Star catalogues 1000",

[df.knowledge_scholar_flags_5.astronomy_star_color_classification] = "Astronomy, -, Stellar Spectroscopy",

[df.knowledge_scholar_flags_5.astronomy_star_magnitude_classification] = "Astronomy, -, Star magnitude classification",

[df.knowledge_scholar_flags_5.astronomy_shape_of_the_world] = "Astronomy, -, Shape of the world",

[df.knowledge_scholar_flags_5.astronomy_precession_of_equinoxes] = "Astronomy, -, Precession of equinoxes",

[df.knowledge_scholar_flags_5.astronomy_method_empirical_observation] = "Astronomy, Method, Empirical observation",

[df.knowledge_scholar_flags_5.astronomy_method_path_models] = "Astronomy, Method, Path models"},

[6] = {[df.knowledge_scholar_flags_6.naturalist_method_dissection] = "Naturalist, Method, Dissection",

[df.knowledge_scholar_flags_6.naturalist_observation_anatomy] = "Naturalist, Observation, Anatomy",

[df.knowledge_scholar_flags_6.naturalist_theory_comparative_anatomy] = "Naturalist, Theory, Comparative anatomy",

[df.knowledge_scholar_flags_6.naturalist_theory_classification_by_physical_features] = "Naturalist, Theory, Physical taxonomy",

[df.knowledge_scholar_flags_6.naturalist_observation_migration_patterns] = "Naturalist, Observation, Migration patterns",

[df.knowledge_scholar_flags_6.naturalist_observation_reproductive_behavior] = "Naturalist, Observation, Reproductive behavior",

[df.knowledge_scholar_flags_6.naturalist_observation_foraging_behavior_and_diet] = "Naturalist, Observation, Foraging behavior",

[df.knowledge_scholar_flags_6.naturalist_theory_food_chain] = "Naturalist, Theory, Food chain",

[df.knowledge_scholar_flags_6.naturalist_observation_social_behavior] = "Naturalist, Observation, Social behavior",

[df.knowledge_scholar_flags_6.naturalist_observation_diseases] = "Naturalist, Observation, Veterinary medicine",

[df.knowledge_scholar_flags_6.naturalist_theory_climactic_adaptation] = "Naturalist, Theory, Climactic adaptation",

[df.knowledge_scholar_flags_6.naturalist_observation_embriological_development] = "Naturalist, Observation, Embryology",

[df.knowledge_scholar_flags_6.naturalist_theory_struggle_for_existence] = "Naturalist, Theory, Struggle for existence"},

[7] = {[df.knowledge_scholar_flags_7.chemistry_classification_combustibles] = "Chemistry, Classification, Combustibles",

[df.knowledge_scholar_flags_7.chemistry_classification_ores] = "Chemistry, Classification, Ores",

[df.knowledge_scholar_flags_7.chemistry_metallurgy_alloys] = "Chemistry, Metallurgy, Alloys",

[df.knowledge_scholar_flags_7.chemistry_classification_scratch_test] = "Chemistry, Classification, Scratch test",

[df.knowledge_scholar_flags_7.chemistry_classification_elemental_theory] = "Chemistry, Classification, Elemental theory",

[df.knowledge_scholar_flags_7.chemistry_chemicals_adhesives] = "Chemistry, Chemicals, Adhesives",

[df.knowledge_scholar_flags_7.chemistry_laboratory_blast_furnace] = "Chemistry, Laboratory, Blast furnace",

[df.knowledge_scholar_flags_7.chemistry_laboratory_alembic] = "Chemistry, Laboratory, Alembic",

[df.knowledge_scholar_flags_7.chemistry_laboratory_theory_of_liquid_liquid_extraction] = "Chemistry, Laboratory, Theory of liquid liquid extraction",

[df.knowledge_scholar_flags_7.chemistry_laboratory_theory_of_distillation] = "Chemistry, Laboratory, Theory of distillation",

[df.knowledge_scholar_flags_7.chemistry_laboratory_theory_of_evaporation] = "Chemistry, Laboratory, Theory of evaporation",

[df.knowledge_scholar_flags_7.chemistry_classification_alkali_and_acids] = "Chemistry, Classification, Alkali and acids",

[df.knowledge_scholar_flags_7.chemistry_laboratory_systematic_experiments] = "Chemistry, Laboratory, Systematic experiments",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_flask] = "Chemistry, Laboratory, Glass flask",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_beaker] = "Chemistry, Laboratory, Glass beaker",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_vial] = "Chemistry, Laboratory, Glass vial",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_funnel] = "Chemistry, Laboratory, Glass funnel",

[df.knowledge_scholar_flags_7.chemistry_laboratory_crucible] = "Chemistry, Laboratory, Crucible",

[df.knowledge_scholar_flags_7.chemistry_chemicals_nitric_acid] = "Chemistry, Chemicals, Spirit of niter",

[df.knowledge_scholar_flags_7.chemistry_chemicals_sulfuric_acid] = "Chemistry, Chemicals, Oil of vitriol",

[df.knowledge_scholar_flags_7.chemistry_chemicals_aqua_regia] = "Chemistry, Chemicals, Aqua regia",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_ampoule] = "Chemistry, Laboratory, Glass ampoule",

[df.knowledge_scholar_flags_7.chemistry_laboratory_glass_retort] = "Chemistry, Laboratory, Glass retort",

[df.knowledge_scholar_flags_7.chemistry_laboratory_lab_ovens] = "Chemistry, Laboratory, Lab ovens"},

[8] = {[df.knowledge_scholar_flags_8.geography_surveying_basic] = "Geography, Surveying, Basic",

[df.knowledge_scholar_flags_8.geography_surveying_staff] = "Geography, Surveying, Surveying staff",

[df.knowledge_scholar_flags_8.geography_cartography_basic] = "Geography, Cartography, Basic",

[df.knowledge_scholar_flags_8.geography_surveying_triangulation] = "Geography, Surveying, Triangulation",

[df.knowledge_scholar_flags_8.geography_surveying_cartographical] = "Geography, Surveying, Cartographical surveying",

[df.knowledge_scholar_flags_8.geography_surveying_land] = "Geography, Surveying, Land surveying",

[df.knowledge_scholar_flags_8.geography_surveying_military] = "Geography, Surveying, Military surveying",

[df.knowledge_scholar_flags_8.geography_surveying_engineering] = "Geography, Surveying, Engineering surveying",

[df.knowledge_scholar_flags_8.geography_cartography_geological] = "Geography, Cartography, Geological",

[df.knowledge_scholar_flags_8.geography_cartography_grid_system] = "Geography, Cartography, Grid system",

[df.knowledge_scholar_flags_8.geography_cartography_distance_scale] = "Geography, Cartography, Distance scale",

[df.knowledge_scholar_flags_8.geography_cartography_height_measurements] = "Geography, Cartography, Height measurements",

[df.knowledge_scholar_flags_8.geography_method_economic_data_collection] = "Geography, Methods, Econometrics",

[df.knowledge_scholar_flags_8.geography_cartography_economic] = "Geography, Cartography, Economic",

[df.knowledge_scholar_flags_8.geography_form_atlas] = "Geography, Form, Atlas",

[df.knowledge_scholar_flags_8.geography_theory_delta_formation] = "Geography, Theory, Delta formation",

[df.knowledge_scholar_flags_8.geography_theory_wind_patterns] = "Geography, Theory, Anemology (Wind patterns)",

[df.knowledge_scholar_flags_8.geography_theory_origin_of_rainfall_from_evap_condense] = "Geography, Theory, Origin of rainfall from evap condense",

[df.knowledge_scholar_flags_8.geography_theory_water_cycle] = "Geography, Theory, Water cycle",

[df.knowledge_scholar_flags_8.geography_theory_latitude_climate_zones] = "Geography, Theory, Latitude climate zones",

[df.knowledge_scholar_flags_8.geography_cartography_accurate_maps] = "Geography, Cartography, Accurate maps",

[df.knowledge_scholar_flags_8.geography_cartography_map_projections] = "Geography, Cartography, Map projections"},

[9] = {[df.knowledge_scholar_flags_9.medicine_theory_disease_and_fouled_water] = "Medicine, Theory, Disease and fouled water",

[df.knowledge_scholar_flags_9.medicine_method_physical_examination] = "Medicine, Method, Physical examination",

[df.knowledge_scholar_flags_9.medicine_method_autopsy] = "Medicine, Method, Autopsy",

[df.knowledge_scholar_flags_9.medicine_theory_prognosis] = "Medicine, Theory, Prognosis",

[df.knowledge_scholar_flags_9.medicine_tool_herbal_remedies] = "Medicine, Tool, Herbal remedies",

[df.knowledge_scholar_flags_9.medicine_tool_animal_remedies] = "Medicine, Tool, Animal remedies",

[df.knowledge_scholar_flags_9.medicine_tool_mineral_remedies] = "Medicine, Tool, Mineral remedies",

[df.knowledge_scholar_flags_9.medicine_tool_bandages] = "Medicine, Tool, Bandages",

[df.knowledge_scholar_flags_9.medicine_theory_disease_classification] = "Medicine, Theory, Pathology",

[df.knowledge_scholar_flags_9.medicine_theory_toxicology] = "Medicine, Theory, Toxicology",

[df.knowledge_scholar_flags_9.medicine_theory_acute_and_chronic_conditions] = "Medicine, Theory, Acute and chronic conditions",

[df.knowledge_scholar_flags_9.medicine_theory_endemic_disease] = "Medicine, Theory, Endemic disease",

[df.knowledge_scholar_flags_9.medicine_theory_epidemic_disease] = "Medicine, Theory, Epidemic disease",

[df.knowledge_scholar_flags_9.medicine_theory_exacerbation] = "Medicine, Theory, Exacerbation",

[df.knowledge_scholar_flags_9.medicine_theory_paroxysm] = "Medicine, Theory, Paroxysm",

[df.knowledge_scholar_flags_9.medicine_theory_relapse] = "Medicine, Theory, Relapse",

[df.knowledge_scholar_flags_9.medicine_theory_convalescence] = "Medicine, Theory, Convalescence",

[df.knowledge_scholar_flags_9.medicine_method_treatment_of_traumatic_injuries] = "Medicine, Method, Traumatic injury treatment",

[df.knowledge_scholar_flags_9.medicine_method_fracture_treatment] = "Medicine, Method, Fracture treatment",

[df.knowledge_scholar_flags_9.medicine_theory_fracture_classification] = "Medicine, Theory, Fracture classification",

[df.knowledge_scholar_flags_9.medicine_tool_traction_bench] = "Medicine, Tool, Traction bench",

[df.knowledge_scholar_flags_9.medicine_method_fracture_immobilization] = "Medicine, Method, Fracture immobilization",

[df.knowledge_scholar_flags_9.medicine_tool_orthopedic_cast] = "Medicine, Tool, Orthopedic cast",

[df.knowledge_scholar_flags_9.medicine_method_surgery_excision] = "Medicine, Method, Surgery excision",

[df.knowledge_scholar_flags_9.medicine_method_surgery_incision] = "Medicine, Method, Surgery incision",

[df.knowledge_scholar_flags_9.medicine_method_hernia_surgery] = "Medicine, Method, Hernia surgery",

[df.knowledge_scholar_flags_9.medicine_method_tracheotomy_surgery] = "Medicine, Method, Tracheotomy surgery",

[df.knowledge_scholar_flags_9.medicine_method_lithotomy_surgery] = "Medicine, Method, Lithotomy surgery",

[df.knowledge_scholar_flags_9.medicine_method_surgery_scraping] = "Medicine, Method, Surgery scraping",

[df.knowledge_scholar_flags_9.medicine_method_surgery_draining] = "Medicine, Method, Surgery draining",

[df.knowledge_scholar_flags_9.medicine_method_surgery_probing] = "Medicine, Method, Surgery probing",

[df.knowledge_scholar_flags_9.medicine_method_surgery_suturing] = "Medicine, Method, Surgery suturing"},

[10] = {[df.knowledge_scholar_flags_10.medicine_method_surgery_ligature] = "Medicine, Method, Surgery ligature",

[df.knowledge_scholar_flags_10.medicine_theory_surgical_models] = "Medicine, Theory, Surgical models",

[df.knowledge_scholar_flags_10.medicine_tool_mud_bags_as_surgical_models] = "Medicine, Tool, Mud bags as surgical models",

[df.knowledge_scholar_flags_10.medicine_tool_plants_as_surgical_models] = "Medicine, Tool, Plants as surgical models",

[df.knowledge_scholar_flags_10.medicine_tool_animals_as_surgical_models] = "Medicine, Tool, Plants as surgical models",

[df.knowledge_scholar_flags_10.medicine_theory_specialized_surgical_instruments] = "Medicine, Theory, Specialized surgical instruments",

[df.knowledge_scholar_flags_10.medicine_tool_forceps] = "Medicine, Tool, Forceps",

[df.knowledge_scholar_flags_10.medicine_tool_scalpel] = "Medicine, Tool, Scalpel",

[df.knowledge_scholar_flags_10.medicine_tool_surgical_scissors] = "Medicine, Tool, Surgical scissors",

[df.knowledge_scholar_flags_10.medicine_tool_surgical_needles] = "Medicine, Tool, Surgical needles",

[df.knowledge_scholar_flags_10.medicine_method_cataract_surgery] = "Medicine, Method, Cataract surgery",

[df.knowledge_scholar_flags_10.medicine_method_cauterization] = "Medicine, Method, Cauterization",

[df.knowledge_scholar_flags_10.medicine_method_anesthesia] = "Medicine, Method, Anesthesia",

[df.knowledge_scholar_flags_10.medicine_theory_pulmonary_medicine] = "Medicine, Theory, Pulmonary medicine",

[df.knowledge_scholar_flags_10.medicine_theory_anatomical_studies] = "Medicine, Theory, Anatomical studies",

[df.knowledge_scholar_flags_10.medicine_theory_classification_of_bodily_fluids] = "Medicine, Theory, Classification of bodily fluids",

[df.knowledge_scholar_flags_10.medicine_theory_eye_anatomy] = "Medicine, Theory, Eye anatomy",

[df.knowledge_scholar_flags_10.medicine_theory_motor_vs_sensory_nerves] = "Medicine, Theory, Motor vs sensory nerves",

[df.knowledge_scholar_flags_10.medicine_theory_nervous_system_function] = "Medicine, Theory, Nervous system function",

[df.knowledge_scholar_flags_10.medicine_theory_reaction_time] = "Medicine, Theory, Reaction time",

[df.knowledge_scholar_flags_10.medicine_theory_blood_vessels] = "Medicine, Theory, Blood vessels",

[df.knowledge_scholar_flags_10.medicine_theory_pulmonary_circulation] = "Medicine, Theory, Pulmonary circulation",

[df.knowledge_scholar_flags_10.medicine_theory_comparative_anatomy] = "Medicine, Theory, Comparative anatomy",

[df.knowledge_scholar_flags_10.medicine_theory_the_voice] = "Medicine, Theory, The voice",

[df.knowledge_scholar_flags_10.medicine_theory_classification_of_muscles] = "Medicine, Theory, Classification of muscles",

[df.knowledge_scholar_flags_10.medicine_theory_classification_of_mental_illnesses] = "Medicine, Classification of mental illnesses",

[df.knowledge_scholar_flags_10.medicine_theory_treatment_of_mental_illnesses] = "Medicine, Treatment of mental illnesses",

[df.knowledge_scholar_flags_10.medicine_tool_dedicated_hospitals] = "Medicine, Tool, Dedicated hospitals",

[df.knowledge_scholar_flags_10.medicine_method_professional_hospital_staff] = "Medicine, Method, Professional hospital staff",

[df.knowledge_scholar_flags_10.medicine_method_specialized_wards] = "Medicine, Method, Specialized wards",

[df.knowledge_scholar_flags_10.medicine_method_hospital_lab] = "Medicine, Method, Hospital lab",

[df.knowledge_scholar_flags_10.medicine_method_medical_school] = "Medicine, Method, Medical school"},

[11] = {[df.knowledge_scholar_flags_11.medicine_method_asylum_for_mentally_ill] = "Medicine, Method, Asylum for mentally ill"},

[12] = {[df.knowledge_scholar_flags_12.engineering_horology_shadow_clock] = "Engineering, Horology, Shadow clock",

[df.knowledge_scholar_flags_12.engineering_horology_water_clock] = "Engineering, Horology, Water clock",

[df.knowledge_scholar_flags_12.engineering_horology_conical_water_clock] = "Engineering, Horology, Conical water clock",

[df.knowledge_scholar_flags_12.engineering_horology_water_clock_reservoir] = "Engineering, Horology, Water clock reservoir",

[df.knowledge_scholar_flags_12.engineering_horology_astrarium] = "Engineering, Horology, Astrarium",

[df.knowledge_scholar_flags_12.engineering_horology_hourglass] = "Engineering, Horology, Hourglass",

[df.knowledge_scholar_flags_12.engineering_horology_mechanical_clock] = "Engineering, Horology, Mechanical Clock",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_pulley] = "Engineering, Machine, Theory of pulley",

[df.knowledge_scholar_flags_12.engineering_machine_pulley] = "Engineering, Machine, Pulley",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_screw] = "Engineering, Machine, Theory of screw",

[df.knowledge_scholar_flags_12.engineering_machine_screw] = "Engineering, Machine, Screw",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_wheel_and_axle] = "Engineering, Machine, Theory of wheel and axle",

[df.knowledge_scholar_flags_12.engineering_machine_windlass] = "Engineering, Machine, Windlass",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_wedge] = "Engineering, Machine, Theory of wedge",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_lever] = "Engineering, Machine, Theory of lever",

[df.knowledge_scholar_flags_12.engineering_machine_lever] = "Engineering, Machine, Lever",

[df.knowledge_scholar_flags_12.engineering_machine_straight_beam_balance] = "Engineering, Machine, Straight beam balance",

[df.knowledge_scholar_flags_12.engineering_machine_theory_of_gears] = "Engineering, Machine, Theory of gears",

[df.knowledge_scholar_flags_12.engineering_machine_warded_lock] = "Engineering, Machine, Warded lock",

[df.knowledge_scholar_flags_12.engineering_machine_tumbler_lock] = "Engineering, Machine, Tumbler lock",

[df.knowledge_scholar_flags_12.engineering_machine_padlock] = "Engineering, Machine, Padlock",

[df.knowledge_scholar_flags_12.engineering_machine_camshaft] = "Engineering, Machine, Camshaft",

[df.knowledge_scholar_flags_12.engineering_machine_crankshaft] = "Engineering, Machine, Crankshaft",

[df.knowledge_scholar_flags_12.engineering_machine_water_powered_sawmill] = "Engineering, Machine, Water powered sawmill",

[df.knowledge_scholar_flags_12.engineering_machine_chariot_odometer] = "Engineering, Machine, Chariot odometer",

[df.knowledge_scholar_flags_12.engineering_machine_chain_drive] = "Engineering, Machine, Chain drive",

[df.knowledge_scholar_flags_12.engineering_machine_mechanical_compass] = "Engineering, Machine, Mechanical compass",

[df.knowledge_scholar_flags_12.engineering_machine_differential_gear] = "Engineering, Machine, Differential gear",

[df.knowledge_scholar_flags_12.engineering_machine_combination_lock] = "Engineering, Machine, Combination lock",

[df.knowledge_scholar_flags_12.engineering_machine_verge_escapement] = "Engineering, Machine, Verge escapement",

[df.knowledge_scholar_flags_12.engineering_machine_balance_wheel] = "Engineering, Machine, Balance wheel",

[df.knowledge_scholar_flags_12.engineering_fluid_theory_of_siphon] = "Engineering, Fluid, Theory of siphon"},

[13] = {[df.knowledge_scholar_flags_13.engineering_fluid_valves] = "Engineering, Fluid, Valves",

[df.knowledge_scholar_flags_13.engineering_fluid_force_pump] = "Engineering, Fluid, Force pump",

[df.knowledge_scholar_flags_13.engineering_optics_crystal_lens] = "Engineering, Optics, Crystal lens",

[df.knowledge_scholar_flags_13.engineering_optics_water_filled_spheres] = "Engineering, Optics, Water filled spheres",

[df.knowledge_scholar_flags_13.engineering_optics_glass_lens] = "Engineering, Optics, Glass lens",

[df.knowledge_scholar_flags_13.engineering_optics_camera_obscura] = "Engineering, Optics, Camera obscura",

[df.knowledge_scholar_flags_13.engineering_optics_parabolic_mirror] = "Engineering, Optics, Parabolic mirror",

[df.knowledge_scholar_flags_13.engineering_optics_theory_of_color] = "Engineering, Optics, Theory of color",

[df.knowledge_scholar_flags_13.engineering_optics_theory_of_rainbows] = "Engineering, Optics, Theory of rainbows",

[df.knowledge_scholar_flags_13.engineering_optics_law_of_refraction] = "Engineering, Optics, Law of refraction",

[df.knowledge_scholar_flags_13.engineering_design_models_and_templates] = "Engineering, Design, Models and templates",

[df.knowledge_scholar_flags_13.engineering_construction_wood_lamination] = "Engineering, Construction, Wood lamination",

[df.knowledge_scholar_flags_13.engineering_astronomy_dioptra] = "Engineering, Astronomy, Dioptra",

[df.knowledge_scholar_flags_13.engineering_astronomy_astrolabe] = "Engineering, Astronomy, Astrolabe",

[df.knowledge_scholar_flags_13.engineering_astronomy_armillary_sphere] = "Engineering, Astronomy, Armillary sphere",

[df.knowledge_scholar_flags_13.engineering_astronomy_spherical_astrolabe] = "Engineering, Astronomy, Spherical astrolabe",

[df.knowledge_scholar_flags_13.engineering_astronomy_mural_instrument] = "Engineering, Astronomy, Mural instrument",

[df.knowledge_scholar_flags_13.engineering_astronomy_orrery] = "Engineering, Astronomy, Orrery",

[df.knowledge_scholar_flags_13.engineering_machine_water_powered_trip_hammer] = "Engineering, Machine, Water powered trip hammer",

[df.knowledge_scholar_flags_13.engineering_machine_double_acting_piston_bellows] = "Engineering, Machine, Double acting piston bellows",

[df.knowledge_scholar_flags_13.engineering_fluid_archimedes_principle] = "Engineering, Fluid, Archimedes principle",

[df.knowledge_scholar_flags_13.engineering_optics_atmospheric_refraction] = "Engineering, Optics, Atmospheric refraction",

[df.knowledge_scholar_flags_13.engineering_optics_cause_of_twilight] = "Engineering, Optics, Cause of twilight",

[df.knowledge_scholar_flags_13.engineering_optics_height_of_atmosphere] = "Engineering, Optics, Height of atmosphere",

[df.knowledge_scholar_flags_13.engineering_machine_piston] = "Engineering, Machine, Piston",

[df.knowledge_scholar_flags_13.engineering_machine_crank] = "Engineering, Machine, Crank",

[df.knowledge_scholar_flags_13.engineering_machine_bellows] = "Engineering, Machine, Bellows",

[df.knowledge_scholar_flags_13.engineering_machine_water_powered_piston_bellows] = "Engineering, Machine, Water powered piston bellows",

[df.knowledge_scholar_flags_13.engineering_machine_water_wheel] = "Engineering, Machine, Water wheel",

[df.knowledge_scholar_flags_13.engineering_machine_trip_hammer] = "Engineering, Machine, Trip hammer"}}

Zmap_itemid_to_writing_title = {} -- GLOBAL, PERSISTANT

Zmap_itemid_to_writing_author_name = {} -- GLOBAL, PERSISTANT

Zmap_itemid_to_writing_author_hfid = {} -- GLOBAL, PERSISTANT

Zmap_itemid_to_written_content_id = {} -- GLOBAL, PERSISTANT

Zmap_itemid_to_artifact_id = {} -- GLOBAL, PERSISTANT

local function Zget_title( item )

local function Zcompare_items(item1, item2)