{"id":"W2035842006","doi":"10.1016/j.tpb.2007.02.001","title":"A general framework for marker-assisted selection","year":2007,"lang":"en","type":"article","venue":"Theoretical Population Biology","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Selection (genetic algorithm); Biology; Epistasis; Diallel cross; Population; Index selection; Genetics; Additive genetic effects; Genetic marker; Quantitative trait locus; Evolutionary biology; Heritability; Gene; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003749662,0.0001319128,0.0001304862,0.00003977209,0.00009579842,0.000007944303,0.0001104771,0.0003819991,0.0001100688],"category_scores_gemma":[0.0002899924,0.0001184502,0.00008359414,0.0000783185,0.0002059689,0.000001369301,0.00003366448,0.00008865264,0.000006905693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001356186,"about_ca_system_score_gemma":0.00001563772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006999735,"about_ca_topic_score_gemma":0.000008148451,"domain_scores_codex":[0.9989992,0.0000775298,0.0002517919,0.0003098285,0.00005012288,0.000311554],"domain_scores_gemma":[0.9995364,0.00008024622,0.00006684741,0.0001688437,0.00006826086,0.00007938546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004104892,0.00004236389,0.0121686,0.000005363456,0.00002405929,4.116099e-8,0.000009127164,0.00002731274,0.03894801,0.9380324,0.0001594621,0.01017276],"study_design_scores_gemma":[0.0003249868,0.0005700902,0.3031358,0.000003870364,0.0000186028,0.00001075537,0.000007305355,0.00009323334,0.009142016,0.6840089,0.002521627,0.0001628778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4594867,0.00004074561,0.5389644,0.0001050963,0.0002265249,0.0001691728,0.000009487962,0.00001823074,0.0009796671],"genre_scores_gemma":[0.7869839,0.000002685394,0.2116223,0.0003057427,0.0006393082,0.00001692061,0.0002948912,0.0000161589,0.0001180615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3274973,"threshold_uncertainty_score":0.4830257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219062062637461,"score_gpt":0.302746352537451,"score_spread":0.2905557319110764,"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."}}