{"id":"W7043325634","doi":"","title":"In-season performance of European Union wheat forecasts during extreme impacts","year":2018,"lang":"en","type":"article","venue":"Prodinra (INRA Bordeaux-Aquitaine)","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); European union; Crop yield; Winter wheat; Crop; Forecast period; Agriculture; Economic forecasting; Quarter (Canadian coin)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009909337,0.0004097479,0.0004210591,0.00007657857,0.0002624241,0.00009647469,0.0005774358,0.0001509314,0.0005973379],"category_scores_gemma":[0.0002073353,0.0001713779,0.0001277395,0.001105271,0.0001808017,0.0007415256,0.0003141601,0.0002708378,0.0001487322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519673,"about_ca_system_score_gemma":0.00001652791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002686328,"about_ca_topic_score_gemma":0.001932405,"domain_scores_codex":[0.9971482,0.0002612917,0.0005675374,0.0006002989,0.0004695782,0.0009531351],"domain_scores_gemma":[0.9988053,0.00008034427,0.0002931928,0.0002108057,0.0003250329,0.0002852741],"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":[0.0002356488,0.0003252297,0.08181443,0.0001894316,0.000019515,0.00003315274,0.001204694,0.000006828575,0.8858985,0.00007034966,0.001320066,0.02888214],"study_design_scores_gemma":[0.0007350448,0.001425004,0.9295536,0.0004240617,0.00002084917,0.00006549842,0.0005625164,0.0002237287,0.05978785,0.00005970247,0.006572617,0.0005694906],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899792,0.0003485971,8.373569e-7,0.00234476,0.0002516257,0.00061243,0.00005726838,0.0001356592,0.006269597],"genre_scores_gemma":[0.9974703,0.0003594811,0.0000751223,0.0001735444,0.001186904,0.0000177342,0.00008332518,0.000008334584,0.0006252818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8477392,"threshold_uncertainty_score":0.6988589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0368215109889389,"score_gpt":0.2406955858510697,"score_spread":0.2038740748621308,"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."}}