{"id":"W4252726925","doi":"10.1515/iupac.79.1112","title":"Decipol","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics; Organic chemistry","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":[0.0009857697,0.002049869,0.001522425,0.003602208,0.0008477742,0.0030187,0.002594048,0.001637601,0.115554],"category_scores_gemma":[0.006145447,0.0005983308,0.001773292,0.005928129,0.0003599389,0.001895351,0.00234952,0.001862026,0.1669866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001304792,"about_ca_system_score_gemma":0.002419703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415183,"about_ca_topic_score_gemma":0.03076945,"domain_scores_codex":[0.9988369,0.000202551,0.0001756943,0.0004001005,0.0002349689,0.0001497479],"domain_scores_gemma":[0.9979079,0.0005331706,0.0002998954,0.0005507221,0.0005107959,0.0001975524],"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":[0.000140844,0.00001978805,0.001092691,0.001334171,0.00005001783,0.00002294937,0.00002376365,0.0002252612,0.0001354232,0.0006537984,0.9904337,0.005867475],"study_design_scores_gemma":[0.0001827274,0.00001971513,0.002453921,0.0004645529,0.00004490686,0.00005737882,0.00005720594,0.0002027394,0.0002306035,0.001282209,0.9949806,0.00002350613],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001000035,0.0001256169,0.00006194637,0.00006424575,0.00002201611,0.00001367713,0.998518,0.0002289542,0.0008655539],"genre_scores_gemma":[0.0002946725,0.0001246419,0.000234703,0.00008753774,0.000009317521,0.00008704898,0.9982035,0.000069397,0.0008891876],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.884446,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781943932114706,"score_gpt":0.4347837774771454,"score_spread":0.4169643381559984,"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."}}