{"id":"W1925794974","doi":"10.2135/cropsci2014.09.0609","title":"Genetic Improvement Estimates, from Cultivar × Crop Management Trials, Are Larger in High‐Yield Cropping Environments","year":2015,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Cultivar; Biology; Agronomy; Adaptability; Genetic gain; Cropping; Yield (engineering); Crop yield; Crop; Genetic variation; Agriculture; Ecology","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":[0.009557778,0.0005433884,0.0007539992,0.0009027179,0.0002885541,0.0005568696,0.0005067149,0.0004393953,0.001727768],"category_scores_gemma":[0.004819165,0.0001666839,0.0007722556,0.0006742252,0.0003725114,0.0006703477,0.0004765861,0.0009110955,0.0002935253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003380239,"about_ca_system_score_gemma":0.0002108081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000484406,"about_ca_topic_score_gemma":0.001535826,"domain_scores_codex":[0.995196,0.002127114,0.0006182268,0.001046359,0.0008750719,0.0001371908],"domain_scores_gemma":[0.9837458,0.01014461,0.002900244,0.001685718,0.001040133,0.0004835783],"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":[0.00342019,0.00188126,0.1264197,0.0007805235,0.002129669,0.0002381305,0.0003796306,0.003325894,0.7828767,0.0003643884,0.0006611351,0.07752273],"study_design_scores_gemma":[0.0001832512,0.01560917,0.7865476,0.0001038688,0.001758582,0.0003371007,0.0002462852,0.002522808,0.1865172,0.0002377513,0.005867526,0.00006878207],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873869,0.001504219,0.008613372,0.00006805867,0.00003729371,0.0001336115,0.0005968562,0.0001481444,0.001511606],"genre_scores_gemma":[0.9854033,0.0004114666,0.01123261,0.0001420814,0.00001784596,0.0001303915,0.00142001,0.00006304847,0.001179235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009557778,"threshold_uncertainty_score":0.050547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08580642919528055,"score_gpt":0.2466519219390067,"score_spread":0.1608454927437262,"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."}}