{"id":"W6992023768","doi":"","title":"Iowa Eclipses Canada in Grain Production, Challenges China in Soybean Production","year":2011,"lang":"en","type":"dataset","venue":"Issue Lab (Candid)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Agriculture; Production (economics); Crop production; Agricultural productivity; Chine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005807304,0.001898007,0.0009066128,0.002571751,0.001104807,0.002618663,0.002054605,0.001875851,0.03043137],"category_scores_gemma":[0.004050774,0.0006669734,0.001165876,0.005513903,0.0004557084,0.001559366,0.001483641,0.001776228,0.03258451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003879472,"about_ca_system_score_gemma":0.004145687,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4288942,"about_ca_topic_score_gemma":0.6902111,"domain_scores_codex":[0.999324,0.000102969,0.00005286818,0.0001939225,0.0001897022,0.0001365597],"domain_scores_gemma":[0.9983453,0.0002864618,0.0001784343,0.0003457347,0.0006301586,0.0002140021],"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":[0.00002968614,0.00001060452,0.001545151,0.0001094122,0.00001260024,0.000012818,0.00001202398,0.0002123371,0.00003171594,0.0003083605,0.9965557,0.00115971],"study_design_scores_gemma":[0.0001279098,0.00001122758,0.01145234,0.000215265,0.00003488659,0.00006127736,0.0001580179,0.001410495,0.0003322646,0.0006474966,0.9855108,0.00003796139],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005170293,0.0001352788,0.00008071803,0.0003475767,0.00006119611,0.000006490043,0.9964155,0.0002847573,0.002151367],"genre_scores_gemma":[0.001543509,0.0001043135,0.0002268044,0.00008803595,0.00001036182,0.00002008545,0.9961076,0.00005527374,0.00184403],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5711058,"threshold_uncertainty_score":0.8527955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291600513133476,"score_gpt":0.2481871328409097,"score_spread":0.2252711277095749,"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."}}