{"id":"W1975056155","doi":"10.1139/x03-180","title":"Simulation of the comparative gains from four different hybrid tree breeding strategies","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Epistasis; Selection (genetic algorithm); Hybrid; Biology; Range (aeronautics); Tree breeding; Statistics; Tree (set theory); Mathematics; Computer science; Ecology; Agronomy; Machine learning; Genetics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0007631744,0.0004378661,0.0004711301,0.000680269,0.0003286407,0.0005118123,0.0006771413,0.000742718,0.002610138],"category_scores_gemma":[0.003208483,0.0002647026,0.0006801056,0.000583475,0.0004083533,0.0004005013,0.0004465518,0.0005493202,0.0001377477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024349,"about_ca_system_score_gemma":0.0007019907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01297372,"about_ca_topic_score_gemma":0.009539056,"domain_scores_codex":[0.9998172,0.00007476323,0.000008875116,0.00002398472,0.00003137919,0.00004386119],"domain_scores_gemma":[0.9958422,0.003481169,0.0001920173,0.00008830684,0.0002723783,0.0001240671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000784425,0.00003973165,0.002630344,0.0000156598,0.00001546871,0.00002447838,0.00002033339,0.9945309,0.0003613017,0.0007345455,0.0001042336,0.001444621],"study_design_scores_gemma":[0.00001867921,0.00006378035,0.000712669,0.000004300678,0.00001359377,0.000006866293,0.00002091795,0.9984157,0.0002775043,0.0003276074,0.0001327726,0.000005554028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760034,0.0001363762,0.01437089,0.0001392963,0.00002089666,0.00003851789,0.0003309496,0.0001146946,0.00884491],"genre_scores_gemma":[0.9933206,0.00005405979,0.005491028,0.00002444645,0.0000026304,0.00005300063,0.0001809903,0.00001815188,0.0008551818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01297372,"threshold_uncertainty_score":0.02579641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09948080956961512,"score_gpt":0.3428004045579907,"score_spread":0.2433195949883756,"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."}}