{"id":"W2103413860","doi":"","title":"Trends in sire variance estimates by birth year","year":2000,"lang":"en","type":"article","venue":"Bulletin - International Bull Evaluation Service/Interbull bulletin","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sire; Variance (accounting); Econometrics; Trait; Statistics; Variance components; Estimation; Mathematics; Economics; Biology; Animal science; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004669176,0.0001944915,0.0003044249,0.003394223,0.0002840813,0.0006499608,0.0004227472,0.0002498957,0.003297498],"category_scores_gemma":[0.01508942,0.0001809653,0.0003660633,0.002808608,0.0002676366,0.0004242437,0.000368003,0.0007592447,0.0007417582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004992925,"about_ca_system_score_gemma":0.0003629861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01463768,"about_ca_topic_score_gemma":0.02366881,"domain_scores_codex":[0.9981487,0.0006507746,0.000195526,0.0003804147,0.0004341589,0.0001904663],"domain_scores_gemma":[0.9836237,0.007055845,0.002941719,0.001383143,0.004684001,0.0003115381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003441642,0.00003968997,0.9380773,0.0001183714,0.0003134878,0.00006061425,0.001104986,0.001120202,0.002637191,0.000990267,0.004267398,0.05092621],"study_design_scores_gemma":[0.000002125564,0.00008209542,0.9951734,0.00001359376,0.00003139145,0.0001146552,0.0002316923,0.0004627273,0.0007919517,0.0001020589,0.002978999,0.00001527289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565037,0.002700926,0.009580057,0.0003896093,0.00008204614,0.00004211938,0.02077122,0.0004811171,0.009449201],"genre_scores_gemma":[0.9694838,0.001461216,0.007975934,0.00007583686,0.00004355476,0.00005203723,0.0169896,0.0001107563,0.003807279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01463768,"threshold_uncertainty_score":0.02910495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457038731822029,"score_gpt":0.3413189695364152,"score_spread":0.3067485822181949,"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."}}