{"id":"W104766600","doi":"10.2527/2001.7961450x","title":"Estimated genetic parameters for growth traits of German shepherd dog and Labrador retriever dog guides.","year":2001,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heritability; Labrador Retriever; Withers; German Shepherd Dog; Restricted maximum likelihood; Animal science; Litter; Biology; Body weight; Animal model; Awassi; Veterinary medicine; Maximum likelihood; Ecology; Mathematics; Statistics; Medicine; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388749,0.0001126806,0.0001753082,0.00006464805,0.0000826327,0.00002931985,0.0003424898,0.00007215259,0.000009831427],"category_scores_gemma":[0.0002402377,0.00009116352,0.00007117577,0.0002146225,0.000450888,0.00001566154,0.00005807888,0.00006679338,4.231767e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001135666,"about_ca_system_score_gemma":0.0001740469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009713628,"about_ca_topic_score_gemma":0.000002169055,"domain_scores_codex":[0.9989294,0.00001881417,0.0003600805,0.0002082145,0.0002433882,0.000240097],"domain_scores_gemma":[0.9991283,0.00002687345,0.0002592561,0.0001025707,0.0003264038,0.0001565339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002914043,0.00006081413,0.005570739,0.00002261973,0.00002609329,0.000003018613,0.0001571602,0.0001282436,0.9873308,0.0005768288,0.000513164,0.005319129],"study_design_scores_gemma":[0.0008622805,0.003885659,0.8391638,0.00003857115,0.00004819461,0.0003091936,0.00005921804,0.0001476469,0.1525704,0.00223009,0.0005115786,0.0001733305],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928808,0.0006071387,0.005959214,0.0001146499,0.00009546702,0.0001173085,0.000007507296,0.00000230412,0.0002155951],"genre_scores_gemma":[0.8853506,0.00009290966,0.114325,0.00009669007,0.000083917,0.000001187267,6.018191e-7,0.000007838918,0.00004131475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8347604,"threshold_uncertainty_score":0.3717541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02079004232801183,"score_gpt":0.296656622313586,"score_spread":0.2758665799855741,"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."}}