{"id":"W4412749751","doi":"10.1016/j.applanim.2025.106769","title":"Gait tracking in dogs using DeepLabCut: A markerless machine learning approach for controlled settings","year":2025,"lang":"en","type":"article","venue":"Applied Animal Behaviour Science","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Royal Society","keywords":"Gait; Tracking (education); Physical medicine and rehabilitation; Pet therapy; Animal-assisted therapy; Computer vision; Artificial intelligence; Computer science; HUBzero; Psychology; Medicine; Animal welfare; Biology; Pedagogy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009758129,0.0002088526,0.0003196997,0.0002094158,0.0004602593,0.0001352298,0.0003566802,0.00009495219,0.000006272543],"category_scores_gemma":[0.00019148,0.0002018689,0.00009641391,0.0004179751,0.0002650733,0.00001721143,0.0002540638,0.0002155394,0.000001368846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000636638,"about_ca_system_score_gemma":0.0001225582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004462897,"about_ca_topic_score_gemma":0.00004763204,"domain_scores_codex":[0.9982497,0.00003180519,0.0003571825,0.00070312,0.0002010762,0.000457186],"domain_scores_gemma":[0.9994131,0.00004439545,0.0001516718,0.0001762936,0.000158232,0.00005628066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008921725,0.00009801647,0.107599,0.00002070438,0.00001921264,0.000001487616,0.0001451879,0.0002774869,0.8898075,0.0008467822,0.0000324048,0.0002600115],"study_design_scores_gemma":[0.01933939,0.0008466649,0.2350733,0.0001879214,0.0003773547,0.00006765402,0.005409946,0.08442189,0.6504707,0.0001562758,0.001679332,0.001969636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880971,0.0002125211,0.007516994,0.00004600437,0.0000647292,0.0006865012,0.000007339225,0.00002584009,0.003342957],"genre_scores_gemma":[0.9924892,0.000008572484,0.006761744,0.0001919408,0.00004459658,0.0001798238,0.0000168207,0.00001731719,0.0002899364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2393368,"threshold_uncertainty_score":0.8231975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111441251211608,"score_gpt":0.3450162431081313,"score_spread":0.3239018305960152,"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."}}