{"id":"W2029254935","doi":"10.2527/jas.2007-0234","title":"Primary genome scan to identify putative quantitative trait loci for feedlot growth rate, feed intake, and feed efficiency of beef cattle1","year":2007,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture Food and Rural Development; Agriculture and Agri-Food Canada; University of Alberta","funders":"","keywords":"Quantitative trait locus; Residual feed intake; Biology; Sire; Feedlot; Animal science; Microsatellite; Autosome; Beef cattle; Chromosome; Feed conversion ratio; Genetics; Allele; Gene; Body weight","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001680822,0.0001399539,0.0002416435,0.0001684508,0.0001470906,0.00003770294,0.0004264736,0.00006519591,0.000004384739],"category_scores_gemma":[0.0004095972,0.0001180633,0.00008243529,0.0003976702,0.0006620807,0.00002443652,0.0001010137,0.00009874554,7.146078e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004108507,"about_ca_system_score_gemma":0.0003468467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001356832,"about_ca_topic_score_gemma":0.00001178992,"domain_scores_codex":[0.9985862,0.00003501375,0.0004634332,0.0002855217,0.0003009245,0.0003289026],"domain_scores_gemma":[0.9985484,0.00008055307,0.0003550524,0.00009606337,0.0006846148,0.000235286],"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":[0.0008055322,0.00008781585,0.0006055897,0.00002788189,0.00002421263,8.198401e-7,0.001061187,0.0001176428,0.9945742,0.00203931,0.00005864738,0.0005971079],"study_design_scores_gemma":[0.0005321017,0.005471966,0.7398877,0.00001710867,0.0000240636,0.00003489356,0.000791932,0.000004347176,0.2522652,0.0007653577,0.00007601104,0.0001294214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392537,0.0003613065,0.0593699,0.0001345599,0.0001141587,0.0002402896,0.00002959041,0.000002035253,0.0004944073],"genre_scores_gemma":[0.9448084,0.00001094227,0.05489663,0.0001344947,0.00009748877,0.000001874352,0.000003166889,0.000009672114,0.00003733354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7423091,"threshold_uncertainty_score":0.4814482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964173102011938,"score_gpt":0.3065009007796511,"score_spread":0.2868591697595317,"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."}}