{"id":"W2906482906","doi":"10.1017/wet.2018.88","title":"Potential yield loss in sugar beet due to weed interference in the United States and Canada","year":2018,"lang":"en","type":"article","venue":"Weed Technology","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"","keywords":"Sugar beet; Weed; Yield (engineering); Weed control; Agronomy; Sugar; Geography; 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":[0.000846308,0.0003324927,0.0002255034,0.001968235,0.001812028,0.001484866,0.0007107185,0.0001856405,0.002292452],"category_scores_gemma":[0.001593005,0.0001318437,0.0003547355,0.003493845,0.0003092637,0.0003015657,0.0005602452,0.0003694623,0.0002391135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02340977,"about_ca_system_score_gemma":0.02537405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9892481,"about_ca_topic_score_gemma":0.9959757,"domain_scores_codex":[0.9985837,0.00006230915,0.00003925114,0.00007620768,0.001008822,0.0002296544],"domain_scores_gemma":[0.9975343,0.0001325274,0.0002050779,0.00003440537,0.001858381,0.0002352639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008613172,0.0001707683,0.8008291,0.0003141607,0.0005062737,0.0008234837,0.001469791,0.006248523,0.006735981,0.001846248,0.03711386,0.1430806],"study_design_scores_gemma":[0.00002024277,0.00008706294,0.9658263,0.00009800967,0.0001248616,0.0000916682,0.002164963,0.002983157,0.002209604,0.0002452291,0.02609951,0.00004929778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9106721,0.004818266,0.001057712,0.001647959,0.00005586818,0.0001523071,0.02253654,0.0001780199,0.05888121],"genre_scores_gemma":[0.9716367,0.003041842,0.001144736,0.0005144189,0.00001198776,0.00005809824,0.009594333,0.00002721743,0.0139707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02340977,"threshold_uncertainty_score":0.1698505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008718469441441312,"score_gpt":0.2013812030631693,"score_spread":0.192662733621728,"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."}}