{"id":"W4239874646","doi":"10.1515/iupac.79.1324","title":"Gene Map","year":2016,"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":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Multidisciplinary approach; Computer science; Hazard; Toxicology; Biology; Chemistry; Philosophy; Linguistics; Sociology; Social science","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":[0.0009349007,0.001975955,0.002022321,0.003775783,0.0013092,0.003054095,0.003013006,0.002156674,0.1392462],"category_scores_gemma":[0.006482029,0.0007247882,0.001939694,0.005957174,0.0004071956,0.001503936,0.001776291,0.001956661,0.1744356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325935,"about_ca_system_score_gemma":0.003210068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01524574,"about_ca_topic_score_gemma":0.02645087,"domain_scores_codex":[0.9988394,0.0001401642,0.0001234692,0.0005446578,0.0002040522,0.0001482255],"domain_scores_gemma":[0.9982912,0.0005464685,0.0001457575,0.0004263608,0.000434559,0.0001555267],"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":[0.0001766551,0.0000269109,0.002100455,0.001657493,0.00009120384,0.00006116628,0.00004364472,0.0005159712,0.0005243458,0.001053179,0.9861545,0.00759451],"study_design_scores_gemma":[0.0002277741,0.00002988336,0.004393181,0.0003999737,0.0001024783,0.0001365908,0.00007735632,0.0004747306,0.0005060469,0.002550313,0.9910645,0.00003707118],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001243201,0.0001326218,0.0001460825,0.00005803106,0.00002951509,0.00001190614,0.9981571,0.0004593681,0.000881038],"genre_scores_gemma":[0.0003981884,0.0001274072,0.000521794,0.00008250915,0.000007935262,0.00008578964,0.9979593,0.00009259297,0.000724489],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1392462,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131582238021728,"score_gpt":0.3778247910131738,"score_spread":0.364666567211001,"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."}}