{"id":"W3000330919","doi":"10.1007/s00484-019-01858-z","title":"Evaluating the utility of various drought indices to monitor meteorological drought in Tropical Dry Forests","year":2020,"lang":"en","type":"review","venue":"International Journal of Biometeorology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dry season; Environmental science; Wet season; Moderate-resolution imaging spectroradiometer; Precipitation; Vegetation (pathology); Growing season; Climatology; Remote sensing; Geography; Meteorology; Satellite; Agronomy; Medicine; Cartography; Biology","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":[0.002335022,0.001107985,0.002230174,0.002851771,0.0001522329,0.001273169,0.001221287,0.0007916262,0.001086002],"category_scores_gemma":[0.00285833,0.0003647257,0.0009277504,0.003305461,0.0004045233,0.001265864,0.0006010583,0.001232526,0.0004783629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005241208,"about_ca_system_score_gemma":0.001054955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003439534,"about_ca_topic_score_gemma":0.003977231,"domain_scores_codex":[0.9995515,0.00007512928,0.0000613406,0.0001160535,0.0001718675,0.00002418407],"domain_scores_gemma":[0.9978784,0.001346219,0.0002688863,0.00003769164,0.000414228,0.00005465534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001114023,0.00005228983,0.001841991,0.01077448,0.0004746602,0.00003671955,0.00003795017,0.001127531,0.001282393,0.001189272,0.003571331,0.9795001],"study_design_scores_gemma":[0.000254487,0.001121254,0.05766263,0.02555756,0.008166177,0.001735984,0.000616339,0.007282356,0.01040967,0.01387488,0.8728535,0.0004652405],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008669748,0.997637,0.0007487923,0.0001382486,0.00008657209,0.000008172937,0.00009850224,0.000009224622,0.0004064832],"genre_scores_gemma":[0.00598307,0.9921581,0.001248046,0.0001141738,0.0001554104,0.0000102661,0.0001452862,0.000004127447,0.0001816064],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003439534,"threshold_uncertainty_score":0.01234895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05757789640280484,"score_gpt":0.3885977911048235,"score_spread":0.3310198947020186,"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."}}