{"id":"W4412701820","doi":"10.1016/j.ecoinf.2025.103325","title":"A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment","year":2025,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Risk assessment; Computer science; Machine learning; Data science; Artificial intelligence; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004298137,0.001551879,0.0009812098,0.002780444,0.000436773,0.002305499,0.001665916,0.001324738,0.005141836],"category_scores_gemma":[0.0108937,0.0008343231,0.001098829,0.003010406,0.0005288929,0.003724896,0.001419114,0.003200383,0.003761718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127483,"about_ca_system_score_gemma":0.002088963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00352003,"about_ca_topic_score_gemma":0.00343689,"domain_scores_codex":[0.9982845,0.0004290978,0.000177066,0.0002845239,0.0007505788,0.00007413584],"domain_scores_gemma":[0.9950389,0.002686812,0.0001674769,0.0004852339,0.001499343,0.000122243],"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":[0.00006812463,0.0001174822,0.002532976,0.001553937,0.0001148107,0.00006780955,0.0000947713,0.03276686,0.001637717,0.01857306,0.02642452,0.9160479],"study_design_scores_gemma":[0.00003540423,0.0004277387,0.006000515,0.003405211,0.000179679,0.0005269572,0.0002107975,0.3763083,0.01264889,0.1185276,0.4815426,0.0001862331],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007629529,0.09143431,0.8686382,0.005808944,0.0005415853,0.0002578124,0.002006259,0.003771262,0.01991197],"genre_scores_gemma":[0.1072175,0.2046412,0.6606882,0.002830061,0.001186068,0.0005817158,0.008234221,0.00112268,0.01349834],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005141836,"threshold_uncertainty_score":0.02273101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456896091740424,"score_gpt":0.2592223189878942,"score_spread":0.24465335807049,"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."}}