{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001114,0.0002941559,0.0001545773,0.0004795383,0.0002147022,0.0002974493,0.0001497174,0.0001219266,0.0006680539],"category_scores_gemma":[0.001003153,0.0001342371,0.0002089028,0.000327534,0.0001307489,0.0001276389,0.0002165554,0.0001981545,0.0001320242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000104083,"about_ca_system_score_gemma":0.0001575143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003134438,"about_ca_topic_score_gemma":0.006776032,"domain_scores_codex":[0.9997959,0.0001058085,0.000009744941,0.00004759069,0.00002317006,0.0000178573],"domain_scores_gemma":[0.9996062,0.0001988428,0.00007493274,0.00005478601,0.00003448917,0.0000308385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002651958,0.0001906091,0.8809403,0.000026658,0.0002184544,0.001075569,0.0009219099,0.00318946,0.06804743,0.0003990758,0.0001095418,0.04461586],"study_design_scores_gemma":[0.00001521167,0.0001912099,0.9837332,0.0000133172,0.00009435213,0.0005828898,0.0001965991,0.01259486,0.002035739,0.0001112103,0.000416063,0.00001538009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987417,0.000008366213,0.001093114,0.000003216103,6.160718e-7,0.000004212062,0.00003082571,0.000005616434,0.000112431],"genre_scores_gemma":[0.9967265,0.00002127166,0.002882082,0.000005349712,7.233904e-7,0.000009893805,0.0001540404,0.00000442754,0.0001957247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003134438,"threshold_uncertainty_score":0.006232381,"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."}}