{"id":"W6977487922","doi":"10.6084/m9.figshare.4042929.v1","title":"Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach<sup>$</sup>","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hazard; Plan (archaeology); Chemical space; Human health; Hazard analysis; Risk assessment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002791004,0.002202541,0.001389388,0.002822491,0.001212255,0.002949209,0.004622398,0.003030892,0.01560584],"category_scores_gemma":[0.01209595,0.0005125712,0.002228134,0.004096411,0.0008208455,0.0013535,0.00163119,0.002294594,0.0107984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008713884,"about_ca_system_score_gemma":0.008459979,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5454019,"about_ca_topic_score_gemma":0.7242576,"domain_scores_codex":[0.9982749,0.000333988,0.0001166612,0.0004941627,0.0005506967,0.0002295774],"domain_scores_gemma":[0.9954944,0.002003481,0.0002351482,0.0007668978,0.001259398,0.000240757],"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":[0.0003577567,0.0001152165,0.01446771,0.001415912,0.0003038275,0.0001433703,0.00006418714,0.02280352,0.0003573471,0.002841651,0.9429495,0.01417996],"study_design_scores_gemma":[0.0007034393,0.00009385078,0.02197875,0.0007634957,0.0002605997,0.0001829576,0.0002712277,0.03944888,0.002018805,0.007131632,0.9269939,0.0001525337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002590147,0.0005402218,0.0006010337,0.0006293733,0.0000649856,0.000030987,0.9921822,0.0008007686,0.00256024],"genre_scores_gemma":[0.005918718,0.0001839476,0.001518755,0.0001236192,0.000007921493,0.00004809289,0.9913714,0.00005639658,0.0007711016],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4545981,"threshold_uncertainty_score":0.9145507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266963679853993,"score_gpt":0.2458879264855147,"score_spread":0.2132182896869747,"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."}}