{"id":"W2268229410","doi":"10.1002/jpln.201400280","title":"Growth, yield, and yield components of canola as affected by nitrogen, sulfur, and boron application","year":2015,"lang":"en","type":"article","venue":"Journal of Plant Nutrition and Soil Science","topic":"Nitrogen and Sulfur Effects on Brassica","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of New Brunswick; Agriculture and Agri-Food Canada; University of Guelph; Université Laval; Dalhousie University; McGill University","funders":"Agriculture and Agri-Food Canada; McGill University; Dalhousie University; Université Laval","keywords":"Canola; Agronomy; Yield (engineering); Brassica; Nitrogen; Biomass (ecology); Fertilizer; Nutrient; Boron; Crop; Environmental science; Chemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003572047,0.00007525345,0.00013304,0.00006876691,0.00007024311,0.00003210645,0.0000868631,0.00006497435,6.855653e-7],"category_scores_gemma":[0.0001464303,0.00006270024,0.00002050274,0.00008956528,0.0002218457,0.00002465665,0.00003750651,0.00006994654,2.214256e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007269555,"about_ca_system_score_gemma":0.00006019469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009477314,"about_ca_topic_score_gemma":0.00001963894,"domain_scores_codex":[0.9993479,0.00002838689,0.0001679803,0.000141319,0.0002023669,0.0001120501],"domain_scores_gemma":[0.9994459,0.00002627016,0.0001470185,0.00006193748,0.0001393311,0.000179519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001153466,0.00009250332,0.004387656,0.00002629172,0.000008698756,0.000003225387,0.00003142657,1.874789e-7,0.993673,0.00005647758,0.001127024,0.0004782105],"study_design_scores_gemma":[0.00117072,0.0007126504,0.004903786,0.00008236316,0.00002022058,0.0005467266,0.0001402296,0.00008912857,0.9907309,0.0003218837,0.001170759,0.0001106217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997298,0.002040735,0.0001465678,0.0002253499,0.00004434896,0.00009296544,0.00003592194,0.000002115113,0.000114008],"genre_scores_gemma":[0.9982297,0.001458116,0.0001229225,0.0001153759,0.00004403394,0.000002508362,0.00001803819,0.000003234326,0.000006084472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002942041,"threshold_uncertainty_score":0.2556841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351627996514417,"score_gpt":0.2339497532011093,"score_spread":0.2204334732359651,"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."}}