{"id":"W4231283221","doi":"10.1515/iupac.76.0178","title":"Concentration–Response Curve","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Multidisciplinary approach; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Political science; Linguistics; Law; Philosophy","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":[0.00278191,0.002283415,0.002156007,0.004970087,0.0005815692,0.003084243,0.0030978,0.002281087,0.1003546],"category_scores_gemma":[0.02156685,0.0008151155,0.00267466,0.006698188,0.0003943453,0.001935072,0.001635587,0.002176174,0.1049962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890055,"about_ca_system_score_gemma":0.002548243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117213,"about_ca_topic_score_gemma":0.01749028,"domain_scores_codex":[0.9964529,0.0005842927,0.0006843874,0.001267012,0.0008317155,0.000179712],"domain_scores_gemma":[0.9896003,0.004702298,0.00139696,0.001971678,0.002034643,0.0002941406],"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":[0.0002595464,0.00008555357,0.003265737,0.003758415,0.0001592633,0.00003704973,0.00002963933,0.0008914258,0.0003634756,0.00110504,0.9757286,0.01431622],"study_design_scores_gemma":[0.0003014576,0.00006476384,0.008065262,0.0009421974,0.000115895,0.0001198935,0.00004505599,0.0008657988,0.0006989074,0.002570488,0.9861437,0.00006657986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001624831,0.0002354266,0.0002985426,0.00005934831,0.00003737208,0.00003669442,0.9978368,0.000491966,0.0008414363],"genre_scores_gemma":[0.0007935425,0.0002773497,0.001109756,0.0001472912,0.00001846495,0.0002659681,0.9960614,0.0001336252,0.001192511],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1003546,"threshold_uncertainty_score":0.3357194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006719450379492,"score_gpt":0.3524714541037592,"score_spread":0.3424042595999643,"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."}}