{"id":"W4237532563","doi":"10.1515/iupac.79.1579","title":"Maximum Exposure Limit (MEL)","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; Hazard; Toxicology; Computer science; Chemistry; Philosophy; Biology; Linguistics","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":[0.001802773,0.001648168,0.001767116,0.004182868,0.0005567518,0.002377928,0.002648305,0.001712833,0.07053918],"category_scores_gemma":[0.01189027,0.0005354744,0.002134362,0.005159969,0.0003515492,0.002429415,0.001389306,0.00177786,0.06391764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681235,"about_ca_system_score_gemma":0.002081051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01817825,"about_ca_topic_score_gemma":0.02324003,"domain_scores_codex":[0.9977235,0.0003557847,0.0005217655,0.0007782896,0.0004712738,0.0001494287],"domain_scores_gemma":[0.9958406,0.00143561,0.0006713906,0.0007491224,0.001157826,0.0001454116],"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":[0.0003192762,0.00004977587,0.004288508,0.003407762,0.0001714688,0.00005259149,0.00003179494,0.001480261,0.0002366124,0.001197225,0.9723771,0.01638755],"study_design_scores_gemma":[0.0002931091,0.00005908092,0.009047508,0.001014174,0.0001369779,0.000146288,0.00008115766,0.0008925403,0.0005790723,0.003041641,0.9846469,0.0000616453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002250521,0.0002776092,0.0002113155,0.00007111878,0.00004389305,0.00002880513,0.9973284,0.0002811976,0.001532657],"genre_scores_gemma":[0.001697335,0.0003560792,0.0008370269,0.0001875556,0.00002397163,0.0001921427,0.9951594,0.00008914596,0.001457191],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07053918,"threshold_uncertainty_score":0.2359771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050949467552094,"score_gpt":0.3297230478951317,"score_spread":0.3192135532196108,"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."}}