{"id":"W4412369258","doi":"10.3390/meteorology4030018","title":"Systematic Biases in Tropical Drought Monitoring: Rethinking SPI Application in Mesoamerica’s Humid Regions","year":2025,"lang":"en","type":"article","venue":"Meteorology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vicerrectoría de Investigación, Universidad de Costa Rica; Universidad Nacional Autónoma de México; Universidad de Costa Rica; Consejo Superior Universitario Centroamericano; International Development Research Centre","keywords":"Tropics; Temperate climate; Precipitation; Climatology; Environmental science; Tropical climate; Wet season; Subtropics; Geography; Ecology; Meteorology; Biology; Cartography; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504857,0.0001283213,0.0004325314,0.0002196142,0.00007673029,0.000008134548,0.0002899616,0.0002017946,0.00007674581],"category_scores_gemma":[0.000406839,0.0001171776,0.00007007081,0.0009813387,0.0002187998,0.00008737826,0.000132239,0.0002738819,0.0001528802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019934,"about_ca_system_score_gemma":0.00001204568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115013,"about_ca_topic_score_gemma":0.002988645,"domain_scores_codex":[0.9982126,0.0004741915,0.000459093,0.0004007505,0.000119131,0.0003341731],"domain_scores_gemma":[0.9990906,0.0004017042,0.0000882142,0.0003767339,0.000004257759,0.00003855145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003989462,0.0001599876,0.9831235,0.0001684011,0.00004294007,0.00004273623,0.0006819631,0.009042089,0.002357516,0.003851341,0.00008226487,0.0004074101],"study_design_scores_gemma":[0.001243259,0.0001689668,0.8859787,0.0005589139,0.0002721614,0.00002498563,0.0004066172,0.05172365,0.00233471,0.05568637,0.001133917,0.0004677411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884931,0.0003102738,0.004662601,0.002132694,0.0001174214,0.0003186039,4.237743e-7,0.00004197732,0.003922869],"genre_scores_gemma":[0.9980169,0.00004279892,0.0009685589,0.0003885156,0.00002151172,0.0002104565,0.000003360067,0.000006072634,0.0003418487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09714475,"threshold_uncertainty_score":0.4778362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717754951197001,"score_gpt":0.2775929380240033,"score_spread":0.2604153885120333,"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."}}