{"id":"W2071982401","doi":"10.1155/2010/823018","title":"Forest Fire Risk Assessment: An Illustrative Example from Ontario, Canada","year":2010,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Wilfrid Laurier University; Western University","funders":"Mitacs; Institute for Catastrophic Loss Reduction","keywords":"Ignition system; Environmental science; Meteorology; Boreal; Documentation; Taiga; Computer science; Statistics; Geography; Mathematics; Engineering; Forestry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004675245,0.0004657424,0.0002843951,0.001082496,0.002055592,0.0009440999,0.0009942482,0.0004435983,0.002010723],"category_scores_gemma":[0.001225258,0.0001390172,0.0005373886,0.002548047,0.0004826825,0.000232993,0.0004277988,0.0003340897,0.000124226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02458089,"about_ca_system_score_gemma":0.01610286,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9910941,"about_ca_topic_score_gemma":0.9945236,"domain_scores_codex":[0.9995725,0.00006542414,0.00001534298,0.00003190957,0.0001851907,0.0001296431],"domain_scores_gemma":[0.9994118,0.0001506823,0.00003974012,0.0000212775,0.0003154555,0.00006089933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004982862,0.0001739696,0.2720878,0.0004449442,0.0002684415,0.004412189,0.002267347,0.5973272,0.002349369,0.02786719,0.01485472,0.07744868],"study_design_scores_gemma":[0.0001826431,0.0002075246,0.3337767,0.0001946069,0.0003254254,0.0009769693,0.007365237,0.6032363,0.001710342,0.01070205,0.04113208,0.0001900461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.92676,0.001230804,0.009231384,0.001116414,0.00002572844,0.0002493965,0.004899159,0.0002473191,0.05623976],"genre_scores_gemma":[0.9865808,0.0007663071,0.004779226,0.0000502628,0.000007247399,0.00003024627,0.001126699,0.0000192978,0.00663984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02458089,"threshold_uncertainty_score":0.1783476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009780796151872679,"score_gpt":0.218296077593294,"score_spread":0.2085152814414213,"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."}}