{"id":"W6894206245","doi":"10.5683/sp3/tspboc","title":"Replication Data for: Weed control, environmental impact and profitability with glyphosate tank mixes in glyphosate-tolerant corn","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glyphosate; Profitability index; Replication (statistics); Weed; Environmental impact assessment; Weed control","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002616433,0.001761337,0.001458283,0.001463072,0.0006152402,0.001499172,0.002719718,0.002259145,0.03496418],"category_scores_gemma":[0.01211063,0.0005682238,0.001914295,0.002840929,0.0003933882,0.0007777772,0.00128107,0.001438395,0.02625356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381365,"about_ca_system_score_gemma":0.002283963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04128016,"about_ca_topic_score_gemma":0.06502932,"domain_scores_codex":[0.9983454,0.0003797867,0.0002036927,0.0005619785,0.0003342308,0.0001749672],"domain_scores_gemma":[0.9960394,0.001256677,0.0006679376,0.0008993896,0.0009080767,0.0002284972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006627404,0.000105988,0.006863647,0.002631932,0.0004776129,0.0000699619,0.00004543989,0.001791167,0.0005080155,0.0005468577,0.9812051,0.005091672],"study_design_scores_gemma":[0.004002575,0.0002206043,0.04280791,0.0008930266,0.0006767475,0.0001737788,0.0001497294,0.002389455,0.0012618,0.002239882,0.9450615,0.0001230093],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006308181,0.0001131853,0.00007388228,0.00007617493,0.00002084041,0.00001793489,0.9985697,0.0001616612,0.000335791],"genre_scores_gemma":[0.002306856,0.00006689722,0.0004852919,0.00007253575,0.000009910911,0.0002070474,0.9959221,0.00006171494,0.0008675922],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04128016,"threshold_uncertainty_score":0.1169668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02504253152237635,"score_gpt":0.2924127679161097,"score_spread":0.2673702363937333,"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."}}