{"id":"W4388408634","doi":"10.1016/j.vaa.2023.09.026","title":"Machine learning models for prediction of Feline Grimace Scale© scores","year":2023,"lang":"en","type":"article","venue":"Veterinary Anaesthesia and Analgesia","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Medicine; Scale (ratio); Machine learning; Artificial intelligence; Computer science; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002878432,0.0002329081,0.0003658356,0.0002037641,0.0003266108,0.00002141799,0.0001236932,0.00009188605,0.00001644222],"category_scores_gemma":[0.00001789511,0.0002109265,0.0001465757,0.0002960842,0.0001315435,0.0002135896,0.0001053873,0.0001304238,0.000008054353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001451428,"about_ca_system_score_gemma":0.0000124002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008767777,"about_ca_topic_score_gemma":0.000002564533,"domain_scores_codex":[0.998719,0.00008059711,0.0003416671,0.0003755739,0.0001679324,0.0003151898],"domain_scores_gemma":[0.9995042,0.00005409802,0.0001150146,0.0001668832,0.0000759693,0.0000838526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009800918,0.0006226824,0.5653077,0.002109567,0.0004678337,0.00138731,0.004088771,0.001497278,0.2806655,0.009468186,0.002838195,0.1217461],"study_design_scores_gemma":[0.001431823,0.0150405,0.8977989,0.0002175235,0.0002218753,0.000408162,0.0008592551,0.0746527,0.0002475401,0.00173443,0.006863408,0.0005238736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964721,0.001085948,0.001028901,0.0003375341,0.00005553897,0.0002731862,0.0001721712,0.0002726704,0.0003019532],"genre_scores_gemma":[0.9972723,0.00154155,0.0005539368,0.00002681724,0.00008320421,0.00007108569,0.000154009,0.00004172025,0.0002554464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3324912,"threshold_uncertainty_score":0.8601335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09774809306124092,"score_gpt":0.3156628537681158,"score_spread":0.2179147607068749,"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."}}