{"id":"W2132556219","doi":"10.1029/2001jd900034","title":"An assessment of irrigation needs and crop yield for the United States under potential climate changes","year":2001,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Irrigation; Climate change; Crop yield; Agriculture; Climate model; Growing season; Forcing (mathematics); Crop; Moisture stress; Soil water; Yield (engineering); Water content; Agronomy; Climatology; Moisture; Geography; Meteorology; Soil science; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009867036,0.0001191075,0.0002249329,0.00001430556,0.0003227098,0.0001793243,0.0003297032,0.00007968034,0.00016476],"category_scores_gemma":[0.0001728521,0.00003548671,0.00009328141,0.0007190924,0.0002007966,0.0002101157,0.00008146213,0.0003267908,0.000001021516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004010624,"about_ca_system_score_gemma":0.00001884733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001021798,"about_ca_topic_score_gemma":0.001144535,"domain_scores_codex":[0.9983277,0.0001813269,0.0002616514,0.0001241043,0.0006909983,0.0004142201],"domain_scores_gemma":[0.9970975,0.001527522,0.0002283905,0.00006499243,0.0008780499,0.0002035749],"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":[0.0008835589,0.0009447237,0.006032655,0.00006533982,0.000156892,0.000018542,0.0005327096,0.001037898,0.9205358,0.001998329,0.003339735,0.06445383],"study_design_scores_gemma":[0.0006281405,0.007807873,0.9436157,0.0001661259,0.00007280582,0.00004658536,0.01879399,0.01179998,0.003674415,0.006805369,0.00637542,0.0002136484],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875324,0.0001962872,0.00006339125,0.01178264,0.00005888952,0.000268521,0.00004717333,0.000008138104,0.00004250292],"genre_scores_gemma":[0.9969917,0.001948911,0.000175335,0.0001686919,0.0006092942,0.000009071869,0.00003403255,0.00000206475,0.00006090177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.937583,"threshold_uncertainty_score":0.2482056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09161945737994609,"score_gpt":0.3826096801036201,"score_spread":0.290990222723674,"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."}}