{"id":"W4247161733","doi":"10.1515/iupac.88.1484","title":"Vertebra","year":2017,"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; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","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":[0.0003890355,0.0004539956,0.0006057177,0.0001255043,0.0003353233,0.0001761492,0.000982444,0.0002776795,0.002957552],"category_scores_gemma":[0.001309614,0.0003975373,0.0001915083,0.00004851578,0.0002108435,0.00012291,0.000373516,0.0007434424,0.0000400411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001725443,"about_ca_system_score_gemma":0.0003932372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004747725,"about_ca_topic_score_gemma":0.0002076112,"domain_scores_codex":[0.9977809,0.00008169733,0.0003442352,0.0005761505,0.0008075999,0.0004094293],"domain_scores_gemma":[0.9977941,0.0001015461,0.0004307739,0.001245455,0.0002766956,0.0001514589],"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":[0.0004580583,0.000161955,0.00001705863,0.0001299785,0.000105746,0.001015084,0.0000116752,1.948708e-7,0.00001269693,0.000004492103,0.9956204,0.002462709],"study_design_scores_gemma":[0.0004907279,0.001173236,0.000313747,0.0005303996,0.00009034947,0.0001266172,0.00001824048,0.00002322975,0.000002792473,0.0001607435,0.996612,0.000457979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001407764,0.000598807,0.00002030901,0.0001352897,0.0009399757,0.0001515359,0.9960545,0.0001522436,0.0005395545],"genre_scores_gemma":[0.0006336014,0.0002270377,0.0001167754,0.0001429861,0.001897839,0.000008916779,0.9957159,0.00005183953,0.001205166],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.002917511,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257491887687765,"score_gpt":0.5369415219607597,"score_spread":0.4111923331919832,"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."}}