{"id":"W4235251691","doi":"10.1515/iupac.79.2092","title":"Systemic","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":[],"consensus_categories":[],"category_scores_codex":[0.001624793,0.001882877,0.001454756,0.003697505,0.001136649,0.004270785,0.002756495,0.0017428,0.2200287],"category_scores_gemma":[0.01262696,0.0006425569,0.002087954,0.006743243,0.0003648361,0.003209182,0.002571358,0.001780599,0.2673658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001819893,"about_ca_system_score_gemma":0.003411464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02091871,"about_ca_topic_score_gemma":0.03722399,"domain_scores_codex":[0.9973501,0.0004381412,0.0003919651,0.0009761054,0.0005604069,0.0002832242],"domain_scores_gemma":[0.9944031,0.001316946,0.0005247666,0.001584882,0.001847455,0.0003228259],"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":[0.00008104875,0.0000128736,0.001197797,0.0006373552,0.00003353431,0.00001353003,0.00002299717,0.0001229792,0.00005524694,0.0009726208,0.9897892,0.007060735],"study_design_scores_gemma":[0.00009644133,0.00001157492,0.002514928,0.0004073022,0.00003039234,0.00004582486,0.00007815041,0.0001714272,0.0001202995,0.001605735,0.9948992,0.00001868579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001236726,0.0001224662,0.0001472875,0.000133516,0.0000460977,0.00002452376,0.9957533,0.0004421085,0.003206938],"genre_scores_gemma":[0.0005237075,0.0001331773,0.0005047144,0.0002085183,0.00001934559,0.0001268955,0.9955071,0.0001382823,0.002838193],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2200287,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530919668744106,"score_gpt":0.4113473955741205,"score_spread":0.3960381988866794,"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."}}