{"id":"W4206452966","doi":"10.3389/fpls.2021.780250","title":"Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population","year":2022,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Alberta Canola Producers Commission; Canada First Research Excellence Fund; Saskatchewan Canola Development Commission; University of Saskatchewan","keywords":"Biology; Throughput; Selection (genetic algorithm); Population; Genetics; Biotechnology; Evolutionary biology; Computational biology; Computer science; Medicine; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003623695,0.0003099851,0.0003025032,0.0005512408,0.0003333252,0.0003724518,0.00032875,0.0003018231,0.001090146],"category_scores_gemma":[0.0003914353,0.000193239,0.0003015053,0.0003553558,0.0001879536,0.0001878443,0.0003320673,0.0004879185,0.0006556373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002366219,"about_ca_system_score_gemma":0.0001549769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003689711,"about_ca_topic_score_gemma":0.01180936,"domain_scores_codex":[0.9997461,0.00004167005,0.00001021886,0.0001094067,0.00006534347,0.00002726585],"domain_scores_gemma":[0.9995265,0.0001495286,0.00009643128,0.00008906331,0.00007368885,0.00006485398],"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":[0.00004954807,0.00005436536,0.01159897,0.00003734161,0.00001918985,0.00006924398,0.0001403268,0.0003775322,0.9756731,0.00007595403,0.0001576943,0.01174669],"study_design_scores_gemma":[0.00002643503,0.00100777,0.7653589,0.00003262427,0.000124555,0.001403158,0.0005618101,0.01519211,0.2040828,0.00049368,0.01165221,0.00006390343],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186814,0.000298468,0.07416379,0.00008853587,0.00001611738,0.0002210778,0.003842401,0.0007598342,0.001928382],"genre_scores_gemma":[0.8968423,0.0003116506,0.09240984,0.0001207437,0.00001389462,0.0003936402,0.006854492,0.0002030195,0.002850535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003689711,"threshold_uncertainty_score":0.007336438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571869889009134,"score_gpt":0.21575591403154,"score_spread":0.2000372151414486,"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."}}