{"id":"W4237538827","doi":"10.1515/iupac.87.0586","title":"Rotarod Test","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.001338419,0.001679721,0.001799499,0.002552779,0.0005325972,0.001446449,0.00219656,0.001295962,0.04905242],"category_scores_gemma":[0.01310587,0.0004215255,0.001822639,0.003038589,0.0003163503,0.001393242,0.001031046,0.001840524,0.03627072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254195,"about_ca_system_score_gemma":0.001707793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01371644,"about_ca_topic_score_gemma":0.02417979,"domain_scores_codex":[0.9981486,0.0002364304,0.0006011516,0.0005436144,0.0003551899,0.0001151455],"domain_scores_gemma":[0.9938207,0.001516133,0.001394626,0.0009396055,0.002117175,0.0002117287],"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":[0.001392359,0.000158332,0.04187139,0.004446007,0.0005198505,0.0001447632,0.00005709574,0.0006916414,0.0001863039,0.0007114101,0.9137538,0.03606713],"study_design_scores_gemma":[0.00113089,0.0002941521,0.1732415,0.003020442,0.000721017,0.0009110945,0.0001926174,0.001007259,0.0009751495,0.00466421,0.8136483,0.0001931809],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001481568,0.0006104709,0.0003011625,0.0001693517,0.00006678693,0.0001095895,0.9934509,0.0002659819,0.003544248],"genre_scores_gemma":[0.006724379,0.0008008342,0.0009637176,0.0002759855,0.00005199355,0.0006013297,0.9870917,0.00009769219,0.003392339],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04905242,"threshold_uncertainty_score":0.1640967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08692007839963382,"score_gpt":0.4970422816819777,"score_spread":0.4101222032823439,"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."}}