{"id":"W4245986858","doi":"10.1515/iupac.79.1620","title":"Microcosm","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; CAS Registry Number; Toxicology; Computer science; 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":[],"consensus_categories":[],"category_scores_codex":[0.001304329,0.001768972,0.001379507,0.003047213,0.001031866,0.003207288,0.0031826,0.001353846,0.1158401],"category_scores_gemma":[0.008947222,0.0006887797,0.001743452,0.005519477,0.0004602063,0.002394231,0.002601328,0.001921582,0.1279778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001448902,"about_ca_system_score_gemma":0.002802443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02703819,"about_ca_topic_score_gemma":0.05817463,"domain_scores_codex":[0.9983166,0.000308039,0.0002050828,0.0005649441,0.0004006824,0.0002046608],"domain_scores_gemma":[0.9964306,0.0009668309,0.0003714258,0.001070546,0.0008544137,0.0003062765],"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":[0.0001241281,0.00002071096,0.001467769,0.0007872151,0.00004667831,0.00002112757,0.00002959034,0.0003066022,0.00008100863,0.001109627,0.9899617,0.00604384],"study_design_scores_gemma":[0.0001718535,0.00001849802,0.003345666,0.0003668066,0.00003535491,0.00005239133,0.00007558148,0.0003613964,0.0001942544,0.001757227,0.9935921,0.00002903875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001771423,0.0001051928,0.0001438657,0.00008612312,0.00004131315,0.00002052801,0.9971688,0.0006392453,0.001617728],"genre_scores_gemma":[0.0005988092,0.0001166555,0.0004960214,0.0001122239,0.00001305974,0.00009133083,0.9968441,0.00015841,0.001569399],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1158401,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497980057036317,"score_gpt":0.4196347963865829,"score_spread":0.4046549958162197,"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."}}