{"id":"W4238440538","doi":"10.1515/iupac.88.1415","title":"Theca","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001762936,0.001534144,0.001596618,0.004744128,0.001274816,0.00478919,0.002937092,0.002127321,0.2464716],"category_scores_gemma":[0.01679169,0.0006267871,0.001912547,0.006837111,0.0004704967,0.00322779,0.002946129,0.00209408,0.2580723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002075235,"about_ca_system_score_gemma":0.004684625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01938405,"about_ca_topic_score_gemma":0.03316803,"domain_scores_codex":[0.9972557,0.0004833777,0.0004932587,0.000872529,0.0005804859,0.0003146639],"domain_scores_gemma":[0.9935474,0.001760057,0.0005793911,0.00156887,0.002127653,0.0004166334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000102303,0.00001340351,0.0008164339,0.001185451,0.00002847561,0.00001689343,0.00002526503,0.0001089526,0.00006846866,0.001184467,0.9888359,0.007614135],"study_design_scores_gemma":[0.00009608617,0.000009633128,0.001425093,0.0006784608,0.00002387717,0.00003320241,0.00004910574,0.0001226284,0.0001082226,0.001299196,0.9961376,0.00001685136],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001059672,0.0002176909,0.0001553873,0.0001941471,0.00007936826,0.00004647243,0.9949126,0.0005850353,0.003703237],"genre_scores_gemma":[0.0004821548,0.0002435749,0.0005822104,0.0003004612,0.00002762637,0.000228989,0.9951884,0.0001988469,0.00274772],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7535284,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186300246406727,"score_gpt":0.4304023166257786,"score_spread":0.4117722919851059,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}