{"id":"W4297852447","doi":"10.1101/2022.09.08.507215","title":"Dissection of genotype-by-environment interaction and simultaneous selection for grain yield and stability in faba bean ( <i>Vicia faba</i> L.)","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Agriculture - Saskatchewan","keywords":"Ammi; Biplot; Vicia faba; Stability (learning theory); Statistics; Gene–environment interaction; Grain yield; Best linear unbiased prediction; Mathematics; Selection (genetic algorithm); Biology; Genotype; Yield (engineering); Biotechnology; Agronomy; Computer science; Genetics; Machine learning","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":[0.0004356985,0.0002097994,0.0002543872,0.00003327143,0.0001555338,0.00005878248,0.0001022085,0.0001888928,0.00005610361],"category_scores_gemma":[0.0001073445,0.000132702,0.0000477662,0.0001391665,0.00003786482,0.0000511195,0.000170567,0.0003041728,3.342483e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082227,"about_ca_system_score_gemma":0.00001329359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000647363,"about_ca_topic_score_gemma":0.0002481345,"domain_scores_codex":[0.9987453,0.00005279794,0.0003051069,0.0005405238,0.0001481814,0.000208047],"domain_scores_gemma":[0.9993119,0.0002650102,0.0002194162,0.00007551114,0.00005209641,0.00007610981],"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":[0.00006687811,0.00007959743,0.03945348,0.00009176085,0.00001678465,8.491547e-7,0.00001741966,0.00009900153,0.9599879,0.000009645363,0.00002906616,0.0001476094],"study_design_scores_gemma":[0.0004850725,0.001133231,0.5056546,0.0002240417,0.0001275697,1.823255e-7,0.0002003034,0.006921919,0.4711727,0.00002481059,0.01293005,0.001125524],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997882,0.0004949974,0.00008237216,0.0001249275,0.0002453159,0.0006078142,0.0005279279,0.00003193515,0.000002721374],"genre_scores_gemma":[0.9992014,0.0004166646,0.0001757444,0.00002458041,0.00009009021,0.00008112296,0.000004020073,0.000004364731,0.000002081717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4888152,"threshold_uncertainty_score":0.5411429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806713475516903,"score_gpt":0.195796095429399,"score_spread":0.1777289606742299,"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."}}