{"id":"W4413406877","doi":"10.3168/jds.2025-27057","title":"Predicting sire fertility in artificial insemination of dairy cows by the ability of spermatozoa to bind to oviduct cell aggregates","year":2025,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Reproductive Physiology in Livestock","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Faculty of Arts and Social Sciences, Carleton University; Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade de São Paulo; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Oviduct; Sire; Artificial insemination; Fertility; Insemination; Andrology; Biology; Dairy cattle; Reproduction; Animal science; Sperm; Endocrinology; Pregnancy; Genetics; Medicine; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141615,0.0002509555,0.0002610798,0.000705124,0.0002438503,0.0004865227,0.0001636079,0.0002890537,0.0005431641],"category_scores_gemma":[0.001499372,0.000166156,0.0002326646,0.0003929962,0.0001596248,0.0002132396,0.0002528052,0.000257089,0.00009738211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610973,"about_ca_system_score_gemma":0.0001181806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385293,"about_ca_topic_score_gemma":0.003196841,"domain_scores_codex":[0.9997261,0.0001115601,0.00001953526,0.00004406988,0.00006686785,0.00003186108],"domain_scores_gemma":[0.9993001,0.0003154909,0.0002253343,0.00003063049,0.00005642325,0.00007194166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001916735,0.00002732833,0.9928684,0.00001039463,0.00005868725,0.00004713113,0.00007885696,0.0002138501,0.00393493,0.00001013726,0.00003078896,0.002527923],"study_design_scores_gemma":[0.000003011492,0.0002315795,0.9974121,0.000002987153,0.00002750676,0.0001578358,0.0001403371,0.001157072,0.000762363,0.00001448244,0.00008781026,0.000002966781],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996402,0.0001506264,0.0001092077,0.000005743283,9.723063e-7,0.000001545308,0.00003472725,0.000001209384,0.00005575653],"genre_scores_gemma":[0.9994307,0.00007651471,0.0002766018,0.000007731088,0.000003119382,0.000002734138,0.000116515,6.044015e-7,0.00008546218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002385293,"threshold_uncertainty_score":0.007489383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962885337622905,"score_gpt":0.2695874188631599,"score_spread":0.2499585654869309,"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."}}