{"id":"W2991499610","doi":"10.1016/j.scitotenv.2019.135139","title":"Modeling the effects of precipitation and temperature patterns on agricultural drought in China from 1949 to 2015","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Northwest A and F University","keywords":"Precipitation; Agriculture; Environmental science; Warning system; Agricultural productivity; Early warning system; Climatology; Geography; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004492213,0.0001001544,0.000121478,0.00001950896,0.0001430953,0.0000126999,0.0005050129,0.00003526894,0.0001069553],"category_scores_gemma":[0.00003456704,0.00004175463,0.0000471165,0.0002308134,0.0003708106,0.0001352915,0.0003869247,0.0001378889,0.00005790214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007843767,"about_ca_system_score_gemma":0.000004171781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007282576,"about_ca_topic_score_gemma":0.00003279835,"domain_scores_codex":[0.9988728,0.0001034974,0.00015007,0.0002528657,0.0004520827,0.0001686854],"domain_scores_gemma":[0.9994181,0.00008085992,0.00006888261,0.0003957039,0.000001603871,0.00003489281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001521817,0.00004130367,0.004419941,0.000002901814,0.000006255292,1.342144e-7,0.002899802,0.7364744,0.2559512,0.00002707587,0.00001522236,0.0001465331],"study_design_scores_gemma":[0.0001749788,0.0001218919,0.9225459,0.00003590544,0.00002498993,0.000001075396,0.0002569388,0.0412407,0.0350963,0.0004168431,0.000002036456,0.00008244827],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975674,0.0000338351,0.000009156208,0.001615455,0.00009649654,0.0003611459,0.00000424464,0.000002143916,0.0003101609],"genre_scores_gemma":[0.9995266,0.00001532476,0.00004138848,0.00005561841,0.00001091579,0.00001040322,8.219443e-7,0.000002945199,0.0003360228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9181259,"threshold_uncertainty_score":0.1702704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002638119193892679,"score_gpt":0.1869099209590784,"score_spread":0.1842718017651857,"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."}}