{"id":"W2946831748","doi":"10.1111/eva.12823","title":"Multi‐trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce","year":2019,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval; Gouvernement du Québec; Natural Resources Canada","funders":"Genome Canada","keywords":"Biology; Weevil; Resistance (ecology); Selection (genetic algorithm); Trait; Biotechnology; Quality (philosophy); Ecology; Agronomy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001985191,0.00008167974,0.00008912151,0.00003616666,0.0001760981,0.00000641109,0.0001171375,0.0000632489,0.0004462076],"category_scores_gemma":[0.0000128791,0.00009149035,0.00002760061,0.0002366087,0.00009478651,0.0001402885,0.00007201036,0.00007661469,0.0004115524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879926,"about_ca_system_score_gemma":0.00001301496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001699906,"about_ca_topic_score_gemma":0.002574363,"domain_scores_codex":[0.9992014,0.00003416437,0.0001927856,0.0003252762,0.00007140886,0.0001750212],"domain_scores_gemma":[0.999662,0.00007890278,0.00006249551,0.0001510341,0.000007617405,0.00003788941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001521719,0.0007484821,0.7297388,0.0001870155,0.00003360603,2.773311e-7,0.000236691,0.002272063,0.08054094,0.1752399,0.009876861,0.0009731972],"study_design_scores_gemma":[0.0004394852,0.00002212873,0.9559745,0.000002849439,0.000006114175,0.000001352616,0.00002703469,0.001056511,0.00003533618,0.007652341,0.03467292,0.0001093812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602036,0.0001589351,0.02121367,0.0007542514,0.00007281453,0.002525606,0.00005538786,0.00005921118,0.01495654],"genre_scores_gemma":[0.9766755,0.00003415795,0.01726332,0.0001525637,0.00002209949,0.001166943,0.00003437101,0.000008023752,0.004643088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2262357,"threshold_uncertainty_score":0.528981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008324996034594662,"score_gpt":0.2380104459214116,"score_spread":0.229685449886817,"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."}}