{"id":"W4416296060","doi":"10.3389/fvets.2025.1704031","title":"Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations","year":2025,"lang":"en","type":"article","venue":"Frontiers in Veterinary Science","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministerul Cercetării, Inovării şi Digitalizării; Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare; Department of Agriculture, Nova Scotia","keywords":"Duration (music); Dairy cattle; Resource (disambiguation); Sensor fusion; European union; Support vector machine; Cardinality (data modeling)","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":[0.0006821078,0.0001428643,0.0003513083,0.001086498,0.0003425725,0.00003433408,0.001012917,0.0000524166,0.000008043106],"category_scores_gemma":[0.0003161192,0.0001307335,0.00008546782,0.003336219,0.0006462946,0.0004401878,0.0008635245,0.00009117124,4.861432e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009184051,"about_ca_system_score_gemma":0.0001350279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001190675,"about_ca_topic_score_gemma":0.00002311059,"domain_scores_codex":[0.998511,0.00005128046,0.0003613955,0.0006035083,0.0001914808,0.0002813934],"domain_scores_gemma":[0.9990416,0.00006354936,0.0001007797,0.0006633149,0.00009287911,0.0000378599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001081539,0.001101775,0.8638193,0.0003399657,0.0005711816,0.00003607963,0.006497389,0.001445179,0.05960004,0.005220174,0.004475243,0.05581212],"study_design_scores_gemma":[0.0003095267,0.0004493638,0.6810962,0.00005500585,0.000250705,0.000002961706,0.004825597,0.3119467,0.00005022581,0.00007270466,0.0007739579,0.0001670856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5887386,0.0003321366,0.4088354,0.00007485216,0.0005629533,0.0002970919,0.0003150605,0.0000314737,0.0008123878],"genre_scores_gemma":[0.9160166,0.00001559298,0.0836485,0.00002025827,0.00000943138,0.00003628764,0.000132134,0.000006304859,0.0001148354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.327278,"threshold_uncertainty_score":0.5331156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2164639045265848,"score_gpt":0.4050500844277888,"score_spread":0.188586179901204,"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."}}