{"id":"W2073413340","doi":"10.4141/a03-023","title":"Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Respiratory Research Network","funders":"","keywords":"Artificial insemination; Sire; Breed; Genetic gain; Biology; Biotechnology; Dairy cattle; Best linear unbiased prediction; Progeny testing; Selection (genetic algorithm); Animal science; Statistics; Genetic variation; Genetics; Mathematics; Computer science; Pregnancy; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001723254,0.0003336466,0.0003567211,0.0009447563,0.0003889198,0.0007854069,0.000629434,0.0001840976,0.00128386],"category_scores_gemma":[0.001538076,0.0001134811,0.00017214,0.001193895,0.0004704947,0.0002480756,0.0003523531,0.0003197278,0.0005248102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002376429,"about_ca_system_score_gemma":0.002909499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1184958,"about_ca_topic_score_gemma":0.1746323,"domain_scores_codex":[0.9988009,0.0001809421,0.00004044118,0.0001545047,0.0007533978,0.00006977541],"domain_scores_gemma":[0.9990695,0.0001682147,0.0002200739,0.00005852649,0.0004086164,0.00007504652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004929866,0.0002881745,0.06749528,0.0003293232,0.0001115569,0.0001970011,0.0003446469,0.007692195,0.1450738,0.002400309,0.003095513,0.7724792],"study_design_scores_gemma":[0.0001202921,0.001671994,0.7014162,0.0002832976,0.0003909686,0.00125045,0.0004933067,0.04862235,0.1606961,0.002604729,0.08220967,0.0002406717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.735518,0.009341559,0.2071363,0.001173686,0.0001109913,0.0009045847,0.004298193,0.002089497,0.03942714],"genre_scores_gemma":[0.8130862,0.006881864,0.157988,0.0002084305,0.00004515232,0.000157028,0.002834585,0.0001101968,0.01868859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1184958,"threshold_uncertainty_score":0.2356122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162690075000413,"score_gpt":0.2576326507424341,"score_spread":0.23600574999243,"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."}}