{"id":"W4413982435","doi":"10.3390/f16091417","title":"Fire Danger Climatology Using the Hot–Dry–Windy Index: Case Studies from Portugal","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Climatology; Environmental science; Geography; Meteorology; Physical geography; Geology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006959056,0.0003201185,0.0002956063,0.0009210817,0.0004843117,0.0007882279,0.0004166825,0.0004648906,0.000496434],"category_scores_gemma":[0.001393999,0.0001578141,0.0004791136,0.001445153,0.0003849789,0.0002881231,0.0003607466,0.0003357391,0.0001202755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152884,"about_ca_system_score_gemma":0.0003878237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06970865,"about_ca_topic_score_gemma":0.1437166,"domain_scores_codex":[0.9997301,0.00008948718,0.00002224381,0.00003968021,0.00005611036,0.00006232751],"domain_scores_gemma":[0.999371,0.0002482228,0.0001436676,0.00006696353,0.00008803383,0.0000820697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005250824,0.000622432,0.9013025,0.0003455285,0.0001680809,0.01059384,0.004589412,0.04197451,0.003350322,0.001346502,0.002104832,0.03307689],"study_design_scores_gemma":[0.00003225337,0.0001555643,0.9617617,0.0001145882,0.00004828183,0.001283432,0.006439337,0.0255015,0.001171282,0.000343379,0.003096419,0.00005228019],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976887,0.0001414808,0.0002990302,0.00004268202,0.000005831696,0.00001641731,0.0004014399,0.00001036257,0.001394018],"genre_scores_gemma":[0.9986994,0.0001194614,0.0005898367,0.000008699058,0.000006749811,0.000007985757,0.0003165664,0.000007655208,0.0002435558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06970865,"threshold_uncertainty_score":0.1386058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707880007479979,"score_gpt":0.2850616218304897,"score_spread":0.2679828217556899,"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."}}