{"id":"W4238187397","doi":"10.1515/iupac.79.2082","title":"Sympathomimetic","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; Computer science; Toxicology; 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":[0.0006365312,0.001543328,0.001698764,0.002170401,0.0005435023,0.001993807,0.00161801,0.001415264,0.1069235],"category_scores_gemma":[0.005852904,0.0003834943,0.001758778,0.003449464,0.000239771,0.001253814,0.001168915,0.001571398,0.08261238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008626653,"about_ca_system_score_gemma":0.001861058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008537297,"about_ca_topic_score_gemma":0.02205,"domain_scores_codex":[0.9990593,0.0001336606,0.0002089678,0.000315643,0.0001927929,0.00008962548],"domain_scores_gemma":[0.9981321,0.0006750344,0.0003705423,0.0003303085,0.0003489958,0.0001431663],"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":[0.001255302,0.00007125959,0.004349517,0.006354104,0.0002946091,0.00008279701,0.00002327198,0.0004414028,0.0003705581,0.0007711329,0.9564807,0.02950519],"study_design_scores_gemma":[0.0009434022,0.00009058669,0.01433295,0.001622291,0.0003235695,0.0002875162,0.00005357705,0.0003447639,0.0004921782,0.001821747,0.9796365,0.0000507265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003331491,0.0008242885,0.00007766744,0.00008869688,0.00005261345,0.00003031215,0.9962423,0.0001282682,0.002222697],"genre_scores_gemma":[0.001408409,0.0008244604,0.0004676197,0.0002826417,0.00004105557,0.0001798116,0.9946425,0.00004199356,0.002111485],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1069235,"threshold_uncertainty_score":0.3576949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177228688757937,"score_gpt":0.3803142080999308,"score_spread":0.3685419212123514,"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."}}