{"id":"W4249246120","doi":"10.1515/iupac.76.0257","title":"Internal Dose","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; Toxicokinetics; Relation (database); Hazard; Toxicology; Computer science; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002328086,0.0004521803,0.0005410842,0.0001047764,0.00005001727,0.00003455307,0.0006853024,0.0003952387,0.009236108],"category_scores_gemma":[0.0003521185,0.0003176255,0.0002644276,0.0001225438,0.00008829045,0.00006336535,0.0004962768,0.0007242096,0.00001986232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005812426,"about_ca_system_score_gemma":0.00009391951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001566919,"about_ca_topic_score_gemma":0.00009955418,"domain_scores_codex":[0.9975996,0.00001991096,0.0005034707,0.0005032063,0.0008838024,0.00049003],"domain_scores_gemma":[0.9986416,0.0001019952,0.0001601996,0.00073646,0.0001348484,0.0002249317],"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":[0.0001649009,0.0001419998,6.43321e-7,0.0002237406,0.0001660008,0.00007690981,0.000003537769,0.00001142434,0.0002883327,0.00003577739,0.9924052,0.006481476],"study_design_scores_gemma":[0.0009742251,0.00004525103,0.000003037351,0.000615094,0.0001055322,0.000007060921,0.000004455489,0.00007350936,0.0004097085,0.0001515438,0.9971451,0.0004655016],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002539259,0.0004559745,0.001555378,0.0006006144,0.0007360543,0.0001576163,0.9959762,0.0001491391,0.0003436472],"genre_scores_gemma":[0.00003192999,0.001268874,0.00005327038,0.0002400113,0.001686267,0.00001241966,0.9943787,0.00004722321,0.002281285],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009216245,"threshold_uncertainty_score":0.9999276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009603175975610261,"score_gpt":0.354995983812409,"score_spread":0.3453928078367987,"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."}}