{"id":"W4406912145","doi":"10.1093/tas/txaf011","title":"Validation of proximity loggers to record proximity events among beef bulls","year":2025,"lang":"en","type":"article","venue":"Translational Animal Science","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Agriculture Funding Consortium","keywords":"Telemetry; False positive paradox; Proximity sensor; Statistics; Mathematics; Computer science; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005028179,0.0001354451,0.0001819614,0.0001724616,0.0003275644,0.00002101449,0.0003252649,0.00004642953,0.0001303528],"category_scores_gemma":[0.0001040736,0.0001240114,0.0000793383,0.0009298126,0.0003700211,0.0006086393,0.00007656326,0.00009810171,0.00002233735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005687923,"about_ca_system_score_gemma":0.0001418674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002046129,"about_ca_topic_score_gemma":0.00001948358,"domain_scores_codex":[0.9984144,0.0000386087,0.0003284196,0.0004166646,0.0005330845,0.0002688227],"domain_scores_gemma":[0.9993654,0.0000672724,0.00007730519,0.0001394303,0.0002686469,0.00008192587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005306695,0.0001537573,0.6927081,0.00003792355,0.00001260171,0.000002442983,0.0004645855,0.00002265422,0.2976059,0.005921823,0.00005720725,0.002482311],"study_design_scores_gemma":[0.0001812228,0.000372255,0.98708,0.00004668965,0.000022842,0.000001567485,0.00005289782,0.00009953192,0.01051492,0.001390273,0.0001088231,0.0001290304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908673,0.00001439356,0.001744789,0.0009952984,0.0001133745,0.0005113904,0.00004211967,0.0000493835,0.005662023],"genre_scores_gemma":[0.9979774,0.000001800216,0.001792669,0.00005331583,0.00002188064,0.00003859209,0.000005395383,0.000005214934,0.0001037387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2943718,"threshold_uncertainty_score":0.5057039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05159982159229555,"score_gpt":0.3458460379918892,"score_spread":0.2942462163995936,"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."}}