{"id":"W4239792161","doi":"10.1515/iupac.79.1925","title":"Remediation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Forensic toxicology; Multidisciplinary approach; Hazard; Computer science; Toxicology; Chemistry; Biology; Philosophy; Sociology; Linguistics; Social science","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":[0.002036732,0.002058349,0.0016155,0.004773503,0.001219447,0.003442688,0.003500259,0.002195125,0.1421721],"category_scores_gemma":[0.01545389,0.0005477726,0.002982177,0.006156186,0.0004910813,0.003163986,0.002563149,0.002024934,0.1363254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00192314,"about_ca_system_score_gemma":0.004056063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02067793,"about_ca_topic_score_gemma":0.04305368,"domain_scores_codex":[0.9966052,0.0005632408,0.0006050722,0.001189523,0.0007250957,0.0003119951],"domain_scores_gemma":[0.9935933,0.001628,0.0005957315,0.002065556,0.001833292,0.0002840919],"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":[0.0001405002,0.00003167678,0.002236145,0.001573051,0.00008045243,0.0000320673,0.00002807205,0.0004784782,0.00012518,0.001411131,0.9791702,0.01469318],"study_design_scores_gemma":[0.000132028,0.00001822564,0.002703456,0.0004915133,0.00005147211,0.00005773957,0.00008594854,0.0003832672,0.0002633173,0.002155539,0.9936293,0.00002807649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002315763,0.0003069265,0.0002606447,0.0002118596,0.0001077297,0.00004716887,0.9949815,0.0005577101,0.003294922],"genre_scores_gemma":[0.0008852938,0.0002435111,0.001025314,0.0002328994,0.00002426206,0.00015279,0.9949286,0.0001177492,0.002389522],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1421721,"threshold_uncertainty_score":0.4756129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283714548531119,"score_gpt":0.3836859617297462,"score_spread":0.3708488162444351,"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."}}