{"id":"W2571405432","doi":"10.1017/wet.2016.2","title":"Perspectives on Potential Soybean Yield Losses from Weeds in North America","year":2017,"lang":"en","type":"article","venue":"Weed Technology","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Weed; Weed control; Yield (engineering); Agronomy; Crop; Weed science; Crop yield; 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":[0.00197895,0.000610284,0.0003827797,0.001591222,0.001848892,0.002239708,0.0009121638,0.0007421806,0.004190912],"category_scores_gemma":[0.002371964,0.0001713427,0.0004990665,0.0016729,0.0008949686,0.001405166,0.001119082,0.001327235,0.0003341556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007294815,"about_ca_system_score_gemma":0.005569827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.332528,"about_ca_topic_score_gemma":0.6495237,"domain_scores_codex":[0.9992099,0.000144608,0.00003734479,0.00009351368,0.0003591984,0.0001553453],"domain_scores_gemma":[0.9968854,0.0004894948,0.0003979779,0.00005460792,0.001851293,0.0003212423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007026962,0.0003402693,0.2908634,0.001987786,0.0004134477,0.002869265,0.007311698,0.004644855,0.01588612,0.02042222,0.1752402,0.4793181],"study_design_scores_gemma":[0.00003021161,0.000415564,0.6362613,0.002332887,0.0003150068,0.001332964,0.02412888,0.002763674,0.003081762,0.01209088,0.3170699,0.0001769624],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3906561,0.1612241,0.006932175,0.1966473,0.001464649,0.00008951897,0.007427987,0.0004663054,0.2350919],"genre_scores_gemma":[0.8127366,0.1414207,0.007852178,0.02223649,0.0009589211,0.00009739834,0.002900156,0.00009026939,0.01170733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.332528,"threshold_uncertainty_score":0.6611849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01277851764521004,"score_gpt":0.2200450994409512,"score_spread":0.2072665817957411,"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."}}