{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001527215,0.002693757,0.002050056,0.004178758,0.0008761023,0.003084792,0.002867308,0.001742193,0.04999823],"category_scores_gemma":[0.007091088,0.0008095237,0.002077835,0.006629468,0.0004682576,0.00193801,0.001903369,0.002347342,0.078038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547537,"about_ca_system_score_gemma":0.002150147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01276593,"about_ca_topic_score_gemma":0.02234059,"domain_scores_codex":[0.9977094,0.0003100846,0.0003687297,0.0008883458,0.0005745982,0.0001488537],"domain_scores_gemma":[0.9969483,0.0009020735,0.0004121538,0.0007899558,0.0008006293,0.0001468123],"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":[0.0001980458,0.00007779431,0.003629803,0.003242906,0.0001228898,0.00005987345,0.00004555871,0.0009153922,0.001008688,0.001103719,0.9719257,0.01766957],"study_design_scores_gemma":[0.0001483098,0.00004223613,0.006610954,0.0005401632,0.00008294715,0.0001445122,0.00006492645,0.000819501,0.001564875,0.002092507,0.9878319,0.00005721893],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003698215,0.0004372171,0.0004449991,0.00006075115,0.0000556029,0.00003040846,0.9968412,0.0006443827,0.001115585],"genre_scores_gemma":[0.0006190908,0.0002507684,0.001065713,0.0001050441,0.00001043602,0.0001506453,0.996806,0.0001104825,0.0008818166],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04999823,"threshold_uncertainty_score":0.1672607,"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."}}