{"id":"W4398198919","doi":"10.1016/j.compag.2024.109058","title":"Enhancing welfare assessment: Automated detection and imaging of dorsal and lateral views of swine carcasses for identification of welfare indicators","year":2024,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Saskatchewan","funders":"","keywords":"Welfare; Identification (biology); Dorsum; Animal welfare; Artificial intelligence; Business; Computer vision; Engineering; Computer science; Medicine; Biology; Political science; Anatomy; Ecology; Law","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":[0.0001687247,0.0001349307,0.0002733935,0.0001403104,0.00009337747,0.00003091372,0.00005054829,0.00006639498,0.000001136347],"category_scores_gemma":[0.000006350469,0.0001003863,0.00004440862,0.0002279277,0.00005689231,0.0001234579,0.00006674731,0.0001269006,2.11903e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004051001,"about_ca_system_score_gemma":0.00001123608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005861366,"about_ca_topic_score_gemma":0.00005307259,"domain_scores_codex":[0.9991303,0.00003247285,0.0003635391,0.0002370367,0.0000855357,0.0001511202],"domain_scores_gemma":[0.9996823,0.00003545882,0.0001388635,0.00006092745,0.00006189493,0.00002052547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001393292,0.0001077231,0.03867738,0.003024502,0.0001796882,0.00001319492,0.003340299,0.00001893227,0.9060053,0.005602619,0.0001013026,0.04278978],"study_design_scores_gemma":[0.0005051842,0.0006604955,0.9709029,0.0003184388,0.0001448102,0.00008393913,0.0008650711,0.007852477,0.01713859,0.0001498647,0.00115934,0.0002188429],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989588,0.008587903,0.0011232,0.0002389837,0.00009827763,0.0002586291,0.00003755122,0.00004977382,0.00001769941],"genre_scores_gemma":[0.9992647,0.0004029186,0.0002551317,0.000003195077,0.00001795562,0.00001728379,0.00002579927,0.000009086124,0.000003946387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9322256,"threshold_uncertainty_score":0.4093636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203202559927066,"score_gpt":0.3072382114564496,"score_spread":0.295206185857179,"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."}}