{"id":"W2507215622","doi":"10.1175/jamc-d-16-0258.1","title":"Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Precipitation; Canola; Environmental science; Growing season; Climate change; Crop yield; Yield (engineering); Crop; Agronomy; Climatology; Geography; Ecology; Meteorology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005931015,0.0003163938,0.0002408527,0.001432209,0.0006218191,0.000955132,0.0005400034,0.0001953667,0.001093808],"category_scores_gemma":[0.001313509,0.0001803912,0.0004486475,0.003323996,0.0003547724,0.0002427366,0.0005955018,0.0002879685,0.0002177001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008204493,"about_ca_system_score_gemma":0.005679874,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8937019,"about_ca_topic_score_gemma":0.9615303,"domain_scores_codex":[0.9996763,0.00006548237,0.00002047326,0.00007800796,0.00007463973,0.00008510389],"domain_scores_gemma":[0.9986928,0.0003407247,0.0001726554,0.0001161858,0.0005336253,0.0001440899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001014732,0.00004173009,0.9804072,0.00002040854,0.000250435,0.0001896748,0.0003369979,0.00702449,0.002207593,0.0002929216,0.0006424318,0.008484638],"study_design_scores_gemma":[0.00000266848,0.000007457099,0.9940753,0.000006440037,0.00002310517,0.00001799999,0.0006941308,0.00444931,0.000269977,0.00005067013,0.0003930996,0.000009831756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971421,0.00006880178,0.0001919369,0.00004864383,0.000002278737,0.00001034889,0.001232419,0.000008339966,0.001295071],"genre_scores_gemma":[0.9977539,0.00007414005,0.0001654541,0.00002044839,0.000001130492,0.00001160261,0.001284886,0.000003938826,0.0006843918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1062981,"threshold_uncertainty_score":0.2138483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198878301178686,"score_gpt":0.2570906430515529,"score_spread":0.2372028129336843,"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."}}