{"id":"W3093793074","doi":"10.1002/csc2.20382","title":"Genetic parameters, prediction, and selection in a white Guinea yam early‐generation breeding population using pedigree information","year":2020,"lang":"en","type":"article","venue":"Crop Science","topic":"Livestock and Poultry Management","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Bill and Melinda Gates Foundation","keywords":"Biology; Selection (genetic algorithm); White (mutation); Population; New guinea; Plant breeding; Genetic gain; Biotechnology; Botany; Genetics; Genetic variation; Demography; Machine learning; Gene","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":[0.0001917656,0.00006628857,0.00005812956,0.0000334554,0.0003139285,0.0002428222,0.00009438833,0.00003079978,0.00001389236],"category_scores_gemma":[0.00006731301,0.00003373589,0.00001126162,0.0008388786,0.00005274811,0.001190558,0.00005238136,0.00005148159,0.000004193512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004171284,"about_ca_system_score_gemma":0.000007912906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005262874,"about_ca_topic_score_gemma":0.0001089338,"domain_scores_codex":[0.9992462,0.00002121618,0.0001802345,0.0001873405,0.0002154508,0.0001496027],"domain_scores_gemma":[0.9997738,0.000008579566,0.00007089401,0.00001535954,0.00005924166,0.00007210772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008005016,0.000007505359,0.8022444,0.000007398138,0.00000104968,2.333173e-7,0.00100391,0.007157036,0.1698737,0.00005198004,0.00005228772,0.01959245],"study_design_scores_gemma":[0.00006155452,0.0001392128,0.8459042,0.000006734063,0.000003685767,0.000002281883,0.0001214391,0.1532766,0.0002768372,0.00002509444,0.0001191219,0.00006327222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998485,0.00001193621,0.0006902557,0.0004379327,0.00007533018,0.0001962375,0.000003963104,0.00003393428,0.00006542583],"genre_scores_gemma":[0.9981898,0.000007276012,0.001440244,0.0001748953,0.0001669531,0.000004970134,0.000008928649,3.464922e-7,0.000006562645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1695969,"threshold_uncertainty_score":0.2414516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04185857738197087,"score_gpt":0.2215312923816378,"score_spread":0.1796727149996669,"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."}}