{"id":"W4240115194","doi":"10.1515/iupac.79.1051","title":"Concentration–Effect Relationship","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Multidisciplinary approach; Computer science; Hazard; Toxicology; Chemistry; Biology; Philosophy; Linguistics; Social science; Sociology","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":[0.002645084,0.002214556,0.002882976,0.005268281,0.0005423966,0.00293259,0.00323067,0.002235195,0.08341704],"category_scores_gemma":[0.02408308,0.0007704657,0.004701209,0.007194473,0.0003691663,0.002210568,0.001473626,0.002248736,0.05291747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823176,"about_ca_system_score_gemma":0.00284783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156518,"about_ca_topic_score_gemma":0.01763127,"domain_scores_codex":[0.9955427,0.0005901689,0.00103822,0.001696425,0.0009134771,0.0002190182],"domain_scores_gemma":[0.98934,0.00553415,0.001561097,0.001681079,0.001671172,0.0002124544],"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":[0.0007500331,0.0001498773,0.01759092,0.01168723,0.001366954,0.0001077671,0.00004515524,0.001929417,0.0004706076,0.00198683,0.9323322,0.03158297],"study_design_scores_gemma":[0.0007659438,0.0001239013,0.02505795,0.001768653,0.001104024,0.0003881844,0.00004592007,0.001324972,0.0007233026,0.004521004,0.9640809,0.00009520931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003266389,0.0009415956,0.0002837796,0.0001065112,0.00006433945,0.00005005216,0.9968826,0.0002085986,0.001135968],"genre_scores_gemma":[0.003652341,0.001162624,0.001298897,0.0004229089,0.00007551305,0.0004617028,0.9908489,0.0001039319,0.001973111],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08341704,"threshold_uncertainty_score":0.2790577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113588497422882,"score_gpt":0.3566173477161964,"score_spread":0.3454814627419676,"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."}}