{"id":"W3201043766","doi":"10.1017/wet.2021.78","title":"Potential wheat yield loss due to weeds in the United States and Canada","year":2021,"lang":"en","type":"article","venue":"Weed Technology","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"Agricultural Research Service; U.S. Department of Agriculture","keywords":"Yield (engineering); Weed; Weed control; Agronomy; Yield gap; Crop yield; Weed science; Environmental science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006152636,0.00007018248,0.0001070634,0.00002662486,0.0001174702,0.00002563535,0.0002153672,0.00009118727,0.00007936676],"category_scores_gemma":[0.00003856587,0.00002635729,0.00001318062,0.0007696808,0.00004851689,0.00001551941,0.0000763771,0.0001420452,0.000005957971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001707344,"about_ca_system_score_gemma":0.00002081063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7968236,"about_ca_topic_score_gemma":0.9543479,"domain_scores_codex":[0.999413,0.00002329224,0.0001102255,0.000176068,0.00007801982,0.0001993775],"domain_scores_gemma":[0.9997215,0.0001053741,0.00001836691,0.00007354795,0.00004654679,0.00003463171],"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":[0.00001453349,0.00006426681,0.004483756,0.000003311778,0.00001502838,0.0002198673,0.00007606018,0.00002891324,0.9355357,0.01575393,0.004336783,0.0394679],"study_design_scores_gemma":[0.0003254526,0.0002101709,0.6252097,0.00002183841,0.00002507285,0.0003095581,0.007875427,0.0001634531,0.009707601,0.02178067,0.3340361,0.0003348858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8566126,0.0001086121,0.000009695655,0.1428747,0.00002282324,0.0001248694,0.0000178828,0.00003403884,0.0001947915],"genre_scores_gemma":[0.9967055,0.000009628435,0.00003506661,0.002964626,0.00003852382,0.00007278399,0.00004258196,5.046376e-7,0.0001307468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.925828,"threshold_uncertainty_score":0.2045294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006993427721978167,"score_gpt":0.1891570556175967,"score_spread":0.1821636278956185,"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."}}