{"id":"W4233520248","doi":"10.1515/iupac.76.0140","title":"Bioconcentration","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Bioconcentration; Relation (database); Toxicology; Computer science; Medicine; Environmental chemistry; Chemistry; Pharmacology; Biology; Data mining; Philosophy; Linguistics; Bioaccumulation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000223498,0.0002498673,0.0002757949,0.000109777,0.0001036691,0.0001687771,0.0008405302,0.0001567655,0.0003208377],"category_scores_gemma":[0.0002250486,0.0001845158,0.00008377299,0.0001825913,0.00005625995,0.000200818,0.0002322905,0.000202723,0.000006826694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846813,"about_ca_system_score_gemma":0.0006411472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002717563,"about_ca_topic_score_gemma":0.00002723111,"domain_scores_codex":[0.9977747,0.00005826243,0.0003612253,0.0005155221,0.0009887164,0.00030156],"domain_scores_gemma":[0.9985102,0.0001782592,0.0001521491,0.0005431903,0.0004770177,0.0001391211],"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":[0.000008813121,0.00005515234,3.51158e-7,0.00002281537,0.00001942247,0.00001894282,0.000004135117,0.00002564654,0.000001643345,0.005224572,0.9722556,0.02236287],"study_design_scores_gemma":[0.0002936012,0.0000803671,0.000007015406,0.0001242712,0.00001347872,0.000008135097,9.864585e-7,0.006265274,0.000005770959,0.02148923,0.9714485,0.000263391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[9.280819e-7,0.0001151484,0.4627952,0.0007442684,0.0007377125,0.00007342985,0.5354682,0.00005428892,0.00001088397],"genre_scores_gemma":[0.00002334062,0.0001178005,0.006047002,0.0006036735,0.001184067,0.000009896619,0.9919502,0.000009204323,0.00005486291],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4567482,"threshold_uncertainty_score":0.7524337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767314285869922,"score_gpt":0.3918131703678291,"score_spread":0.3741400275091299,"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."}}