{"id":"W4400019076","doi":"10.1371/journal.pone.0305671","title":"Automated monitoring of brush use in dairy cattle","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Dairy Farmers of Manitoba; University of British Columbia; Mitacs; Dairy Farmers of Canada","keywords":"Brush; Fiducial marker; Computer science; Artificial intelligence; Identification (biology); Machine learning; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006536726,0.0002626913,0.000451959,0.001046311,0.0002002833,0.0004384707,0.0005190614,0.0004244518,0.0004300657],"category_scores_gemma":[0.001293455,0.0001619391,0.0001701704,0.0008178428,0.0001999258,0.0003552777,0.0003014182,0.0002874739,0.0002242814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003737591,"about_ca_system_score_gemma":0.0002959532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00340368,"about_ca_topic_score_gemma":0.007291828,"domain_scores_codex":[0.999326,0.0001806238,0.00002691132,0.0002208003,0.000185097,0.00006056566],"domain_scores_gemma":[0.9987263,0.0003949573,0.0003519239,0.00008634586,0.0003731872,0.00006724007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005623336,0.000339065,0.5493457,0.000361564,0.0001859792,0.0000920058,0.0004830149,0.01076081,0.1892184,0.0002057913,0.0007808152,0.2476645],"study_design_scores_gemma":[0.00002453133,0.0008578866,0.8267737,0.00005478999,0.0001250129,0.0002454195,0.0003038608,0.135632,0.03365741,0.0003899899,0.001873748,0.00006164837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509926,0.0004486311,0.04645328,0.00004359021,0.00001891441,0.00005808401,0.0005351427,0.0003792534,0.001070436],"genre_scores_gemma":[0.9623829,0.0002023648,0.03635486,0.00004455123,0.00001557951,0.00005722575,0.0004167684,0.0000178794,0.0005078278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00340368,"threshold_uncertainty_score":0.00676775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1819111724812816,"score_gpt":0.3435613791070861,"score_spread":0.1616502066258046,"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."}}