{"id":"W4234036154","doi":"10.1515/iupac.76.0394","title":"Subchronic Effect","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Nonlinear Dynamics and Pattern Formation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Hazard; Relation (database); Computer science; Toxicology; 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"],"consensus_categories":[],"category_scores_codex":[0.0008013101,0.0003964329,0.0004613028,0.0002332966,0.0001143387,0.0002205752,0.001494445,0.0002935638,0.0003375961],"category_scores_gemma":[0.0001011775,0.0002792075,0.0001872715,0.0002215089,0.00005138083,0.0003405715,0.0004621153,0.0004036767,0.00002039218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004384021,"about_ca_system_score_gemma":0.0005376869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005291686,"about_ca_topic_score_gemma":0.0003377802,"domain_scores_codex":[0.9975306,0.0001297008,0.0004038243,0.0005056608,0.0009858122,0.0004444],"domain_scores_gemma":[0.9978416,0.0001207777,0.0002779271,0.00138925,0.0002280872,0.0001423574],"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":[0.00001436741,0.00004365677,0.000007073371,0.0001234918,0.00003478397,0.00003029178,0.000005112317,0.000002086979,0.000002844652,0.00008204178,0.9760463,0.02360799],"study_design_scores_gemma":[0.0006559366,0.0003766685,0.00002001725,0.0002524207,0.00002614995,0.000026546,3.177501e-7,0.001662764,0.00001802484,0.000258305,0.9963344,0.0003684871],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009318013,0.0002615718,0.04777395,0.0004464657,0.001474238,0.000240886,0.9495593,0.0001187685,0.00003167598],"genre_scores_gemma":[0.00003469483,0.0003418718,0.0002742685,0.0002401958,0.0006721205,0.00001054778,0.9982615,0.0000205561,0.0001442516],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04870223,"threshold_uncertainty_score":0.999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007132649194800201,"score_gpt":0.3602707482243929,"score_spread":0.3531380990295926,"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."}}