{"id":"W3026556562","doi":"10.1016/j.jhydrol.2020.125097","title":"Assessing spatiotemporal characteristics of drought and its effects on climate-induced yield of maize in Northeast China","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"“Young Talents” Project of Northeast Agricultural University; China Postdoctoral Science Foundation; Natural Science Foundation of Qinghai; National Natural Science Foundation of China","keywords":"Dryness; Environmental science; Precipitation; Evapotranspiration; Climate change; Yield (engineering); China; Climatology; Geography; Ecology; Meteorology; Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004292541,0.0001102399,0.0005285661,0.0000888755,0.00003221659,0.000005299077,0.000146243,0.000136381,0.0001598654],"category_scores_gemma":[0.000231448,0.00009166408,0.00008137058,0.0002012933,0.00008007907,0.0002001153,0.00007771629,0.0003142312,0.000009502513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002076362,"about_ca_system_score_gemma":0.00001485865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002616403,"about_ca_topic_score_gemma":0.00003497245,"domain_scores_codex":[0.9987628,0.0001625862,0.0005739141,0.0001413977,0.0001795506,0.0001797521],"domain_scores_gemma":[0.9989906,0.0001742434,0.0006540992,0.00008111996,0.00001159303,0.00008837727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004165856,0.0001598687,0.8993763,0.00007142268,0.00005462757,0.000204718,0.0007674537,0.0006031144,0.09498229,0.00004259624,0.00001671935,0.003304363],"study_design_scores_gemma":[0.000791273,0.001468513,0.9700194,0.00005882487,0.00008934813,0.00006663268,0.00002169031,0.007793443,0.01937753,0.0001617312,0.00004357815,0.0001080373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977977,0.00005228891,0.00004206857,0.001521555,0.00008630489,0.00005533493,0.000001916108,0.000002405325,0.000440388],"genre_scores_gemma":[0.9994193,0.00005585441,0.0001151696,0.0003380856,0.0000604595,5.631846e-7,0.000001165556,0.000006985311,0.000002436804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07560475,"threshold_uncertainty_score":0.3737953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158968382761399,"score_gpt":0.2522704343550636,"score_spread":0.2363735960789237,"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."}}