{"id":"W4237617244","doi":"10.1515/iupac.79.0921","title":"Biological Effect Monitoring (BEM)","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; Computer science; Hazard; Multidisciplinary approach; Toxicology; Chemistry; Biology; Philosophy; Political science; Linguistics; Law","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":[0.002604984,0.002370962,0.001911508,0.00409245,0.000594092,0.002433147,0.003229516,0.002064865,0.05344275],"category_scores_gemma":[0.01266312,0.0006926872,0.002326006,0.005182613,0.0003486845,0.002109396,0.00208866,0.001628681,0.06330695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001514021,"about_ca_system_score_gemma":0.002521908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01010819,"about_ca_topic_score_gemma":0.01786864,"domain_scores_codex":[0.9969552,0.0005714832,0.0005068724,0.0009353729,0.0008549226,0.0001761098],"domain_scores_gemma":[0.9943263,0.001721102,0.001121712,0.001440396,0.001177449,0.0002130279],"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":[0.0005546155,0.00007648167,0.006994879,0.00739736,0.0003563211,0.00006920635,0.0000335386,0.001554796,0.0007639518,0.001450065,0.948224,0.0325248],"study_design_scores_gemma":[0.0002473987,0.00006320406,0.0100918,0.0009154083,0.0001936358,0.0001200652,0.00002912941,0.0008429426,0.001234394,0.002298346,0.9839143,0.00004955713],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002149336,0.000507549,0.0003028542,0.00007364707,0.0000341188,0.00003717318,0.9968899,0.0005616978,0.001378204],"genre_scores_gemma":[0.001243688,0.0004315014,0.001122248,0.0001758065,0.00001569484,0.0001969347,0.9954986,0.00007207272,0.001243367],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05344275,"threshold_uncertainty_score":0.1787838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02007448841985862,"score_gpt":0.4083575691002796,"score_spread":0.3882830806804209,"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."}}