{"id":"W4312032633","doi":"10.1002/csc2.20895","title":"Mega‐environment analysis and breeding for specific adaptation","year":2022,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Brandon University; Agriculture and Agri-Food Canada","funders":"","keywords":"Biplot; Biology; Adaptation (eye); Selection (genetic algorithm); Heritability; Mega-; Gene–environment interaction; Crop; Biotechnology; Adaptability; Plant breeding; Cultivar; Statistics; Genotype; Agronomy; Computer science; Mathematics; Evolutionary biology; Ecology; Genetics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003879981,0.000522974,0.000895709,0.00302486,0.0005404918,0.001667301,0.0009139263,0.0003771292,0.005043875],"category_scores_gemma":[0.01395338,0.0003065665,0.001227983,0.004456803,0.0004934881,0.0008963772,0.001888127,0.001107568,0.000832269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005280116,"about_ca_system_score_gemma":0.0006543109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001963059,"about_ca_topic_score_gemma":0.002860891,"domain_scores_codex":[0.9968923,0.001878024,0.0001443107,0.0006907888,0.00025521,0.0001393812],"domain_scores_gemma":[0.9920941,0.005119679,0.0007335533,0.001319408,0.0004742483,0.0002589763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002205455,0.0002758772,0.3125098,0.00129742,0.003898986,0.001131938,0.001881282,0.1281781,0.0226434,0.06546867,0.04758788,0.4129212],"study_design_scores_gemma":[0.0001652835,0.0004613639,0.3292825,0.0001969867,0.0006705389,0.0004860824,0.001379993,0.4717721,0.009596269,0.09488314,0.09092384,0.0001819755],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3159901,0.0007130828,0.637297,0.001092807,0.0001231692,0.0002350048,0.02715658,0.009715477,0.007676635],"genre_scores_gemma":[0.630265,0.0001736174,0.352664,0.0001243563,0.0000268835,0.0003245203,0.01405084,0.001376795,0.0009939207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005043875,"threshold_uncertainty_score":0.02051955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04947444562530032,"score_gpt":0.2018707471188008,"score_spread":0.1523963014935005,"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."}}