{"id":"W2597877403","doi":"10.22004/ag.econ.235251","title":"Modeling Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan","year":2016,"lang":"en","type":"preprint","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Canola; Precipitation; Yield (engineering); Growing season; Growing degree-day; Environmental science; Agronomy; Crop; Spring (device); Crop yield; Sowing; Geography; Biology; Meteorology","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":[0.0001861409,0.0001309906,0.0002591782,0.00004302556,0.000126364,0.00002656421,0.0001904346,0.0001895814,0.0000545351],"category_scores_gemma":[0.00002220089,0.00006624653,0.00005692847,0.00009411751,0.0001233647,0.0001633684,0.0002931823,0.0002386334,0.000001877859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004504357,"about_ca_system_score_gemma":0.0000305119,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0262701,"about_ca_topic_score_gemma":0.06027244,"domain_scores_codex":[0.9992204,0.00005281502,0.0001488455,0.0003038404,0.00009615778,0.0001779504],"domain_scores_gemma":[0.9995007,0.0001781292,0.00009231432,0.00005898445,0.00008146435,0.00008839907],"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":[0.0004317813,0.0002983843,0.243015,0.0006721377,0.0002380878,0.00002978031,0.01587373,0.003335313,0.5923202,0.004770584,0.0004646202,0.1385503],"study_design_scores_gemma":[0.0003357075,0.0002776066,0.9805713,0.00079831,0.00002743408,0.000002792072,0.01238206,0.001819374,0.002097355,0.001059416,0.0002402044,0.0003883885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966871,0.0001297054,0.000003531109,0.002358897,0.00002781274,0.000188886,0.0004129221,0.000006150432,0.0001850668],"genre_scores_gemma":[0.9989226,0.0007469063,0.00005716499,0.00001985184,0.00004013863,6.274201e-7,0.0000415307,6.468558e-7,0.0001705335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7375563,"threshold_uncertainty_score":0.9802141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829631662510312,"score_gpt":0.2222796579920479,"score_spread":0.1939833413669448,"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."}}