{"id":"W4313388556","doi":"10.1017/wet.2022.88","title":"Potential spring canola yield losses due to weeds in Canada and the United States","year":2022,"lang":"en","type":"article","venue":"Weed Technology","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Alberta Crop Industry Development Fund; Agriculture and Agri-Food Canada","funders":"","keywords":"Canola; Weed; Agronomy; Weed control; Yield (engineering); Agriculture; Crop; Crop yield; Biology; Environmental science; Geography; Agroforestry; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001054848,0.0000702107,0.000128012,0.00004830092,0.0002767176,0.00001525387,0.0002988059,0.00003404376,0.0001255298],"category_scores_gemma":[0.00003008409,0.00002788035,0.00001434471,0.0009084905,0.00006551274,0.0000132566,0.0002517932,0.0002083282,0.000002313929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008002222,"about_ca_system_score_gemma":0.00003609414,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929815,"about_ca_topic_score_gemma":0.9895817,"domain_scores_codex":[0.9993684,0.00003041483,0.0001304523,0.0001661344,0.00009254909,0.0002120771],"domain_scores_gemma":[0.9997141,0.0001348525,0.00003488795,0.00006393061,0.00001956088,0.00003265834],"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":[0.0002634936,0.0000954351,0.0371274,0.000006543526,0.00006142101,0.0001309269,0.000228423,0.002422446,0.7679047,0.06657542,0.00181537,0.1233684],"study_design_scores_gemma":[0.001048213,0.0002574973,0.7712448,0.00001120156,0.0000299676,0.0001024383,0.01060643,0.001719226,0.00203573,0.01940587,0.1931142,0.0004244614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379966,0.0001312899,0.000005727481,0.06142844,0.00003542952,0.0002446672,0.00002278206,0.00004627693,0.00008881846],"genre_scores_gemma":[0.9982682,0.000004289251,0.00001640557,0.001322484,0.00002181549,0.0002876235,0.0000109964,7.211621e-7,0.00006745371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.765869,"threshold_uncertainty_score":0.2128316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006270570434827075,"score_gpt":0.1749409422607478,"score_spread":0.1686703718259207,"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."}}