{"id":"W4409802207","doi":"10.1016/j.eja.2025.127650","title":"A Bayesian framework for crop model calibration: A case study in the US Corn Belt","year":2025,"lang":"en","type":"article","venue":"European Journal of Agronomy","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Algonquin College","funders":"Global Collaborative Research, King Abdullah University of Science and Technology; National Institute of Food and Agriculture; King Abdullah University of Science and Technology; University of Nebraska-Lincoln; Office of Science; U.S. Department of Agriculture; U.S. Department of Energy","keywords":"Agronomy; Calibration; Bayesian probability; Crop; Environmental science; Crop yield; Zea mays; Mathematics; Agricultural engineering; Statistics; Biology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006898859,0.0000696965,0.00009642849,0.00001670105,0.0001402491,0.0001035278,0.000216063,0.00001302896,0.00003299997],"category_scores_gemma":[0.00005386098,0.00002251484,0.00006894499,0.000181686,0.00001537328,0.0001054576,0.00002593341,0.0001378473,8.87046e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001462319,"about_ca_system_score_gemma":0.00001569602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002478756,"about_ca_topic_score_gemma":0.00008822129,"domain_scores_codex":[0.9992004,0.0002080624,0.0003195723,0.00009026456,0.00008343358,0.00009828528],"domain_scores_gemma":[0.9995671,0.000156089,0.0001428345,0.00004230671,0.0000605538,0.00003111836],"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":[0.001219185,0.008405348,0.1558083,0.00008954155,0.0005805055,0.006143796,0.04672696,0.01438995,0.01607771,0.02391218,0.06415852,0.662488],"study_design_scores_gemma":[0.007877825,0.009127116,0.730657,0.0006097542,0.0003045524,0.001141965,0.1431684,0.03706343,0.0006951086,0.01136287,0.05677637,0.001215678],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764304,0.00003439009,0.01822227,0.003616374,0.00007361521,0.000342036,0.000002127768,0.000004457567,0.001274391],"genre_scores_gemma":[0.9967529,0.000002038167,0.001507414,0.001401457,0.000142587,0.000005568188,0.000001282271,6.219062e-7,0.0001861042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6612723,"threshold_uncertainty_score":0.1078697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03987378855367395,"score_gpt":0.2730613037107462,"score_spread":0.2331875151570723,"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."}}