{"id":"W2575226293","doi":"10.2134/agronj2016.06.0364","title":"Assessing the Options to Improve Regional Wheat Yield in Eastern Canada Using the CSM–CERES–Wheat Model","year":2017,"lang":"en","type":"article","venue":"Agronomy Journal","topic":"Crop Yield and Soil Fertility","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Nipissing University; Cégep Saint-Jean-sur-Richelieu; Agriculture and Agri-Food Canada","funders":"Nipissing University","keywords":"Yield (engineering); Cultivar; Agronomy; Winter wheat; Environmental science; Fertilizer; DSSAT; Crop; Agriculture; Spring (device); Crop yield; Mathematics; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004285278,0.0001073286,0.0001141152,0.000006103512,0.001993951,0.0008954747,0.0006091047,0.0000453105,0.00008369178],"category_scores_gemma":[0.00006052293,0.0000308482,0.00007923246,0.00004642305,0.00007038716,0.0003598885,0.0001342063,0.0003462627,0.000001763254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156159,"about_ca_system_score_gemma":0.0001839385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2722869,"about_ca_topic_score_gemma":0.6579682,"domain_scores_codex":[0.9990662,0.00006212158,0.0002142825,0.0001646697,0.000204789,0.0002879387],"domain_scores_gemma":[0.9994174,0.0001491433,0.0001133338,0.0001407938,0.00005976526,0.0001195867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001837606,0.0002110537,0.5098974,0.00001213501,0.0001124298,0.00008308369,0.001798892,0.02058297,0.1214608,0.0008112037,0.01750822,0.3273381],"study_design_scores_gemma":[0.0001463512,0.00003501313,0.9648783,0.00008924615,0.00001802309,0.00007943485,0.002481996,0.02547576,0.0002582701,0.001120272,0.005189069,0.0002283154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754049,0.000102905,0.0001013686,0.02299347,0.0002184032,0.0001121299,0.000004653361,0.000003831911,0.0010584],"genre_scores_gemma":[0.9982953,0.000009886841,0.00009473164,0.0008092798,0.00045636,0.000004332722,9.582974e-7,7.798832e-7,0.0003283771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4549809,"threshold_uncertainty_score":0.9993053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1012411844793859,"score_gpt":0.2975743779228164,"score_spread":0.1963331934434304,"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."}}