{"id":"W4234021933","doi":"10.1515/iupac.79.1658","title":"Multistage Model","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; Hazard; Multidisciplinary approach; Toxicology; Chemistry; Philosophy; Biology; 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":[0.003828304,0.002113314,0.002186782,0.002231471,0.0007356222,0.002601568,0.005165476,0.003047467,0.1061262],"category_scores_gemma":[0.01579307,0.0008506565,0.004723511,0.003000804,0.0005084773,0.002264242,0.001343709,0.003231394,0.04562109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002438072,"about_ca_system_score_gemma":0.003216616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03240128,"about_ca_topic_score_gemma":0.04637232,"domain_scores_codex":[0.9980462,0.0007298989,0.000144071,0.000703507,0.0001450569,0.0002313288],"domain_scores_gemma":[0.9942248,0.003912835,0.0002184112,0.000799079,0.0006743811,0.0001704915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001528392,0.0004260111,0.01207004,0.002154464,0.0007620954,0.0001802222,0.00007778274,0.1465648,0.000186286,0.01463887,0.723185,0.09822609],"study_design_scores_gemma":[0.001963549,0.0003997252,0.006450502,0.0007626386,0.0006442617,0.0002881741,0.0001689368,0.6268804,0.0005978235,0.07861485,0.2830878,0.0001412874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0114507,0.003497941,0.04777712,0.003288275,0.0009310747,0.0008354514,0.9141803,0.005914226,0.01212486],"genre_scores_gemma":[0.05831313,0.00167616,0.04753861,0.001374542,0.0003579671,0.001954796,0.8605381,0.0006923337,0.02755438],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1061262,"threshold_uncertainty_score":0.3550277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710785313867461,"score_gpt":0.3985432041537829,"score_spread":0.3814353510151082,"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."}}