{"id":"W2100901986","doi":"10.1017/s0016672302006055","title":"Marker-assisted estimation of quantitative genetic parameters in rainbow trout, <i>Oncorhynchus mykiss</i>","year":2003,"lang":"en","type":"article","venue":"Genetics Research","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Guelph","funders":"","keywords":"Pairwise comparison; Rainbow trout; Statistics; Markov chain Monte Carlo; Mathematics; Regression; Contrast (vision); Covariance; Estimator; Biology; Computer science; Bayesian probability; Artificial intelligence; Fish <Actinopterygii>; Fishery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001055238,0.0001846523,0.0001657977,0.0004156538,0.0001409605,0.0002701016,0.0003183733,0.0001925582,0.000151585],"category_scores_gemma":[0.001825918,0.0001464223,0.0001522897,0.0003171246,0.0001913926,0.0001919204,0.0001943135,0.0002241957,0.00007504734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005143128,"about_ca_system_score_gemma":0.0004775174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044174,"about_ca_topic_score_gemma":0.02520968,"domain_scores_codex":[0.9997398,0.0001152677,0.00001353278,0.00006001674,0.00005817898,0.00001308457],"domain_scores_gemma":[0.9995198,0.0001621372,0.0001812657,0.00003899651,0.00007365457,0.00002422838],"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":[0.0007859737,0.000236495,0.3627797,0.0001313478,0.0002838835,0.0002807117,0.00060541,0.08249133,0.4415126,0.002070905,0.0003749519,0.1084466],"study_design_scores_gemma":[0.0001107126,0.0005010972,0.5828301,0.00002408196,0.0001376521,0.0003353382,0.0001432583,0.3498197,0.06330167,0.00163616,0.001067181,0.00009308189],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838486,0.00006616279,0.01572989,0.00001871984,0.000002798394,0.00001035552,0.0001184907,0.0000553458,0.00014963],"genre_scores_gemma":[0.965323,0.00006424227,0.03402493,0.000009720495,0.000002754581,0.00002288871,0.0002633129,0.00001343229,0.0002756724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01044174,"threshold_uncertainty_score":0.02076197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06101230383000193,"score_gpt":0.3584037045258384,"score_spread":0.2973914006958364,"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."}}