{"id":"W4249632127","doi":"10.1515/iupac.87.0690","title":"Unconscious","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Historical and Scientific Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Philosophy; Data mining; 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":[],"consensus_categories":[],"category_scores_codex":[0.001553671,0.001209386,0.001066226,0.003647615,0.000797102,0.00323336,0.00204874,0.001486208,0.1474202],"category_scores_gemma":[0.0137929,0.0004910486,0.001546107,0.005719645,0.0003802659,0.002057004,0.002023605,0.001553436,0.1685823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001606173,"about_ca_system_score_gemma":0.003207648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197045,"about_ca_topic_score_gemma":0.02235721,"domain_scores_codex":[0.9976016,0.0004035139,0.0005330523,0.0007828393,0.0004198765,0.0002591295],"domain_scores_gemma":[0.9949542,0.00117772,0.0005969058,0.001253852,0.001658418,0.0003588934],"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":[0.0001465983,0.00002124241,0.00243399,0.00130219,0.00004460282,0.00002581987,0.00003510149,0.0001358946,0.00009977783,0.001218352,0.9853061,0.009230274],"study_design_scores_gemma":[0.0001225115,0.00001641728,0.003812681,0.0006061565,0.00002882996,0.00007587705,0.00008128791,0.0001342087,0.0001880351,0.00129818,0.9936172,0.00001863658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001688078,0.0001291309,0.0001212824,0.0001208067,0.00005312071,0.00003576298,0.9965904,0.0002392579,0.002541374],"genre_scores_gemma":[0.0006162818,0.000133523,0.0004005506,0.0001817758,0.00001915101,0.0001537733,0.9964306,0.00006976196,0.001994724],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1474202,"threshold_uncertainty_score":0.4931696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02495121386986694,"score_gpt":0.4004757566824189,"score_spread":0.3755245428125519,"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."}}