{"id":"W4246863416","doi":"10.1515/iupac.87.0700","title":"Ventricle","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":[0.001283928,0.001273593,0.001101898,0.003100337,0.0009081232,0.00333774,0.002228065,0.001733215,0.1755801],"category_scores_gemma":[0.01162041,0.0005025163,0.001521181,0.004813522,0.0003691533,0.002277038,0.001968395,0.001643456,0.2086235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169995,"about_ca_system_score_gemma":0.003021094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01619558,"about_ca_topic_score_gemma":0.02740322,"domain_scores_codex":[0.9978657,0.0003115032,0.000390666,0.000742248,0.0004347032,0.0002551721],"domain_scores_gemma":[0.995645,0.0009982808,0.0004587879,0.001031431,0.001569744,0.0002966969],"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":[0.0001145862,0.0000167381,0.00168874,0.0008071904,0.00002755643,0.0000209982,0.0000235968,0.0001287956,0.00008562925,0.00103689,0.9888632,0.007186034],"study_design_scores_gemma":[0.0001123487,0.00001495315,0.00319828,0.0005484005,0.00002300962,0.00006754253,0.00007535101,0.000172583,0.0001921968,0.001352827,0.9942239,0.00001852677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001626263,0.0001160285,0.0001183618,0.00014457,0.00005877164,0.00002913984,0.9959265,0.0003102036,0.003133875],"genre_scores_gemma":[0.0005555875,0.0001150955,0.0003685631,0.000204951,0.00001766617,0.0001057067,0.9961717,0.00008402312,0.002376647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1755801,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09629864017337972,"score_gpt":0.5089961300304667,"score_spread":0.412697489857087,"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."}}