{"id":"W4297769730","doi":"10.5267/j.dsl.2022.8.001","title":"AHP and fuzzy logic geospatial approach for forest fire vulnerable zones","year":2022,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Analytic hierarchy process; Geography; Human settlement; Land cover; Elevation (ballistics); Environmental resource management; Fuzzy logic; Thematic map; Geographic information system; Land use; Cartography; Environmental science; Computer science; Civil engineering; Engineering; Operations research; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002647368,0.0001545388,0.0001801485,0.00009448698,0.001674951,0.0001581702,0.0008188273,0.00002762137,0.0002293187],"category_scores_gemma":[0.0003100509,0.0001311382,0.00005470011,0.0007922381,0.0004970016,0.0005092948,0.0007725455,0.000183569,0.00005287909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002464766,"about_ca_system_score_gemma":0.00001469502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004067547,"about_ca_topic_score_gemma":0.00003464973,"domain_scores_codex":[0.9972783,0.00008707213,0.0002389686,0.0007725659,0.001114815,0.0005082747],"domain_scores_gemma":[0.9989573,0.0003705309,0.0001065884,0.0004076676,0.000008626938,0.0001492415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003270748,0.0004540832,0.1710868,0.00005546314,0.00001463737,0.00006303109,0.002353405,0.2008686,0.1448308,0.0009146124,0.1110424,0.3679891],"study_design_scores_gemma":[0.001580243,0.0006059177,0.1307765,0.00001325936,0.00001651768,0.0001702129,0.0005568637,0.8311061,0.0005179787,0.002982825,0.03087942,0.0007941981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474542,0.00002878243,0.04937099,0.001221333,0.0005064671,0.0006656672,0.00001608882,0.00004289104,0.0006935776],"genre_scores_gemma":[0.9723573,0.000001737368,0.02427042,0.002947563,0.00004943534,0.0002458685,0.000006100304,0.00001366735,0.0001079448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6302375,"threshold_uncertainty_score":0.9996247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125923270183964,"score_gpt":0.2377854931275567,"score_spread":0.2251931661091603,"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."}}