{"id":"W4322005074","doi":"10.5194/egusphere-egu23-8169","title":"Multi-annual prediction of drought and heat stress to support decision making in the wheat sector","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos","funders":"","keywords":"Environmental science; Climate extremes; Climate change; Agriculture; Climatology; Precipitation; Preparedness; Food security; Evapotranspiration; Environmental resource management; Heat stress; Extreme weather; Geography; Meteorology; Economics; Atmospheric sciences","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":[0.0007975412,0.0002612287,0.0002301277,0.0003077724,0.0001479283,0.000793066,0.0001656076,0.000403114,0.0009153765],"category_scores_gemma":[0.002063921,0.0001208677,0.0001437603,0.0004103442,0.00009725609,0.0005229222,0.0004245976,0.0003433563,0.0002199775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371618,"about_ca_system_score_gemma":0.0003993062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006191353,"about_ca_topic_score_gemma":0.005177455,"domain_scores_codex":[0.9998937,0.00004543209,0.00001004151,0.00001981701,0.00001693692,0.00001410606],"domain_scores_gemma":[0.9994177,0.0003531213,0.00006550903,0.00003456649,0.00007022987,0.00005895736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002022367,0.00009588107,0.02221752,0.00004261271,0.00004967224,0.00007727328,0.00005011083,0.9201284,0.00420425,0.001217126,0.001605508,0.05010942],"study_design_scores_gemma":[0.000004797928,0.00001761905,0.006582657,0.000003828198,0.000004018565,0.0000053317,0.00003071185,0.9915138,0.0005877241,0.0007597401,0.0004850942,0.000004663564],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8757637,0.000845985,0.1124425,0.001213096,0.0002572066,0.00004651291,0.001664941,0.0008732049,0.006892833],"genre_scores_gemma":[0.9849497,0.0002365271,0.01342831,0.00002441618,0.00003816808,0.00001068194,0.0005388158,0.0000169491,0.0007563644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006191353,"threshold_uncertainty_score":0.01231062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014977399012381,"score_gpt":0.3131945321308235,"score_spread":0.2116967922295854,"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."}}