{"id":"W4241292580","doi":"10.1515/iupac.76.0351","title":"Quantitative Structure – Activity Relationships (QSAR)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; 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":[0.001989215,0.003378502,0.002111117,0.00441574,0.00082864,0.00242648,0.004393573,0.002065334,0.04281701],"category_scores_gemma":[0.007258193,0.0007972905,0.002733491,0.005420073,0.0006138851,0.001353766,0.002282158,0.003038218,0.06058858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455098,"about_ca_system_score_gemma":0.002300464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067831,"about_ca_topic_score_gemma":0.02368661,"domain_scores_codex":[0.9979811,0.0004082531,0.0002995538,0.0005689283,0.0005721287,0.0001699881],"domain_scores_gemma":[0.9971839,0.001258461,0.0003300769,0.0006447749,0.000446353,0.0001364495],"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":[0.0001535359,0.0001131934,0.002270152,0.003329197,0.000156446,0.00007955049,0.00003840132,0.002547855,0.0006685322,0.001559747,0.972584,0.01649942],"study_design_scores_gemma":[0.0003280792,0.00007771842,0.004918764,0.0007999509,0.0001171214,0.0001728417,0.00006850691,0.003004487,0.001347229,0.003473341,0.9856194,0.00007255541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003404832,0.0003870109,0.0006507513,0.00007109332,0.00003550562,0.00004412138,0.9969381,0.0007126639,0.000820214],"genre_scores_gemma":[0.0006184087,0.0002082478,0.001592257,0.00006495479,0.00000684565,0.0002195758,0.9967077,0.00006620395,0.0005157152],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04281701,"threshold_uncertainty_score":0.1432371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04494920621493814,"score_gpt":0.4493900108077419,"score_spread":0.4044408045928037,"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."}}