{"id":"W4232442374","doi":"10.1515/iupac.87.0706","title":"Vertebra","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; Relation (database); Chemical nomenclature; Computer science; 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":[0.0007054469,0.001419373,0.001259523,0.004290179,0.0009574627,0.003766367,0.002258183,0.001647232,0.2561192],"category_scores_gemma":[0.007257165,0.0006089169,0.001482878,0.006595709,0.000388209,0.002611866,0.002360434,0.0017179,0.3282279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001658434,"about_ca_system_score_gemma":0.002485777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02037195,"about_ca_topic_score_gemma":0.03754465,"domain_scores_codex":[0.9987211,0.000167261,0.0002306593,0.0004416919,0.0002660935,0.0001732214],"domain_scores_gemma":[0.997169,0.0006772669,0.000326696,0.0006280679,0.0009630627,0.0002359658],"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":[0.00006700956,0.00001062669,0.0009180569,0.0008488526,0.0000218382,0.00001819348,0.00002358443,0.0001261037,0.0000767676,0.0009527255,0.990022,0.006914186],"study_design_scores_gemma":[0.00004763293,0.000007156953,0.00159504,0.0003246576,0.00001148122,0.0000375659,0.00004646805,0.00009878295,0.00009666209,0.001055741,0.9966679,0.00001090396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008806865,0.0001285936,0.00009778646,0.0001039029,0.00004789685,0.00001537687,0.9955999,0.0004933591,0.003425218],"genre_scores_gemma":[0.0004020706,0.0001760672,0.0004196307,0.0001520508,0.00001816483,0.00005600965,0.9954756,0.0001655463,0.003134878],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2561192,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09341892424564942,"score_gpt":0.5007230413392945,"score_spread":0.4073041170936451,"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."}}