{"id":"W4255647310","doi":"10.1515/iupac.79.1696","title":"No-Effect Level (NEL)","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; Multidisciplinary approach; Hazard; Toxicology; Chemistry; Philosophy; Biology; Linguistics; Sociology; 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":[0.00311769,0.001772538,0.002630181,0.004037579,0.0008160854,0.003204551,0.003350326,0.002258328,0.183908],"category_scores_gemma":[0.02905474,0.0007411491,0.003589859,0.004435772,0.0005437466,0.002965986,0.002251842,0.002541609,0.101349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001576302,"about_ca_system_score_gemma":0.003160988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008332547,"about_ca_topic_score_gemma":0.01644807,"domain_scores_codex":[0.9955836,0.0006814767,0.001056858,0.001487533,0.0008353593,0.0003550833],"domain_scores_gemma":[0.986057,0.006333434,0.002046522,0.002849962,0.002218404,0.0004947177],"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":[0.0006685551,0.00006573438,0.004648328,0.008016405,0.0004217342,0.00006463428,0.00003194966,0.0005081706,0.0002440878,0.001363858,0.9706172,0.01334946],"study_design_scores_gemma":[0.000622145,0.00008321129,0.008405187,0.001801995,0.0003512944,0.0001491346,0.00004496148,0.0003148454,0.0003632676,0.003618641,0.9841888,0.0000565892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001660937,0.0003361334,0.000159746,0.00008595356,0.00007337869,0.00005355435,0.9974709,0.0002279882,0.001426385],"genre_scores_gemma":[0.00201519,0.00039294,0.001035061,0.0004778776,0.00006650863,0.0004692572,0.9925724,0.0002040323,0.002766588],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.183908,"threshold_uncertainty_score":0.6152334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899175111028908,"score_gpt":0.3935245636650443,"score_spread":0.3745328125547553,"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."}}