{"id":"W3090845272","doi":"10.1101/2020.09.12.294744","title":"The chaos in calibrating crop models","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"BonaRes; National Science Fund for Distinguished Young Scholars; China Scholarship Council; National Key Research and Development Program of China; Institut National de la Recherche Agronomique; Higher Education Discipline Innovation Project; Priority Academic Program Development of Jiangsu Higher Education Institutions; Bundesministerium für Bildung und Forschung; Academy of Finland; National Institute of Food and Agriculture; Deutsche Forschungsgemeinschaft; U.S. Department of Agriculture; University of Southern Queensland; Agriculture and Agri-Food Canada; National Science Foundation","keywords":"Calibration; Computer science; Software; Function (biology); Data mining; Baseline (sea); Machine learning; Statistics; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0325242,0.001050066,0.00110264,0.001898158,0.0009485834,0.003111999,0.001958607,0.001812797,0.001344732],"category_scores_gemma":[0.1168358,0.001057747,0.0009183497,0.001621893,0.001969034,0.004328468,0.003268157,0.003082081,0.0005416173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002421256,"about_ca_system_score_gemma":0.003124987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00438742,"about_ca_topic_score_gemma":0.0037908,"domain_scores_codex":[0.9758651,0.016435,0.001185981,0.001798047,0.004197477,0.0005183565],"domain_scores_gemma":[0.9337476,0.04282263,0.00376755,0.01126031,0.007993075,0.0004087855],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000092587,0.0001077381,0.01033397,0.0003677866,0.0001799619,0.0001705452,0.001357787,0.7775788,0.004109848,0.09953314,0.0029925,0.1031753],"study_design_scores_gemma":[0.00003769425,0.0001457236,0.002697036,0.0003543211,0.00005271256,0.00009158484,0.0005085046,0.8194814,0.007048005,0.1570199,0.01246985,0.00009319084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.0385802,0.0002635087,0.9536859,0.00177823,0.0000591571,0.0001377221,0.0001044118,0.0005925928,0.004798325],"genre_scores_gemma":[0.4056351,0.0004385947,0.5914935,0.000399358,0.00004674067,0.0003179545,0.0001996087,0.0004632643,0.001005856],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9674758,"threshold_uncertainty_score":0.1720064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479156574533492,"score_gpt":0.2232845655802095,"score_spread":0.1784929998348746,"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."}}