{"id":"W3157855968","doi":"10.1007/s11356-021-14100-4","title":"Persistence and spatial–temporal variability of drought severity in Iran","year":2021,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Precipitation; Teleconnection; Arid; Persistence (discontinuity); Spatial variability; Environmental science; Climatology; Aridity index; Physical geography; Geography; Ecology; Meteorology; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005214961,0.0001604531,0.0002435747,0.001102759,0.0002709417,0.0004083307,0.0003332584,0.0002050208,0.0005145604],"category_scores_gemma":[0.0008483819,0.0001766225,0.0003096632,0.001498365,0.0003515406,0.0003377433,0.0003316243,0.0002502366,0.0001049219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007193218,"about_ca_system_score_gemma":0.000674285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03500054,"about_ca_topic_score_gemma":0.03111293,"domain_scores_codex":[0.9997851,0.00002902185,0.00002343924,0.00005182455,0.00003726018,0.00007323999],"domain_scores_gemma":[0.9993811,0.0001121698,0.0002329077,0.00004529694,0.0001461763,0.00008230096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001184429,0.00004557732,0.992912,0.00001210566,0.0000866523,0.000082828,0.0004139167,0.0007019368,0.0007313505,0.0001317569,0.0002671485,0.004496195],"study_design_scores_gemma":[0.000002738545,0.00001692045,0.998958,0.000001148352,0.00001152773,0.00004382499,0.0002743389,0.0004637352,0.00005526237,0.00002518009,0.0001440017,0.000003282211],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995383,0.00004011219,0.00005824136,0.00002888231,0.000002009922,0.00000171042,0.0001855519,0.000003092244,0.0001422059],"genre_scores_gemma":[0.9995597,0.00003423334,0.00004015259,0.000003974735,0.000005883091,0.00000206971,0.0002837512,8.014401e-7,0.00006941902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03500054,"threshold_uncertainty_score":0.06959361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313767358899052,"score_gpt":0.2918626550274888,"score_spread":0.2604859191375836,"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."}}