{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005153653,0.0004527376,0.0005321603,0.00006348144,0.00007945314,0.00004037274,0.0005027817,0.0004255106,0.007172386],"category_scores_gemma":[0.001040412,0.0003372945,0.0002220334,0.0001633758,0.0001168599,0.00008172479,0.0002417475,0.0005549308,0.00001776355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007116998,"about_ca_system_score_gemma":0.0002276888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004763614,"about_ca_topic_score_gemma":0.00004286415,"domain_scores_codex":[0.9974273,0.00007529398,0.0005409795,0.0005168904,0.0009271344,0.0005123278],"domain_scores_gemma":[0.9982911,0.0003393665,0.0001831911,0.000747296,0.0002069361,0.0002320699],"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":[0.000936625,0.0001363386,9.526713e-7,0.0001431947,0.0001318749,0.00005233748,0.000007010937,0.00003092779,0.0009846666,0.00004113142,0.996119,0.001415976],"study_design_scores_gemma":[0.001081786,0.00005981226,0.000006266383,0.0003475708,0.0001005812,0.000004092966,0.000006851379,0.000115829,0.001062433,0.00008222998,0.9966381,0.0004944332],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001156872,0.0005431258,0.001735738,0.001246907,0.0005653705,0.0002563853,0.9952967,0.0001630777,0.00007698328],"genre_scores_gemma":[0.0001144148,0.0009817225,0.00004223926,0.0002821047,0.0009605797,0.00001917491,0.9962699,0.00004370863,0.001286195],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007154623,"threshold_uncertainty_score":0.9999079,"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."}}