{"id":"W2903359691","doi":"10.1146/annurev-statistics-031017-100450","title":"Statistical Models of Key Components of Wildfire Risk","year":2018,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Natural Resources Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Key (lock); Risk analysis (engineering); Variety (cybernetics); Computer science; Process (computing); Risk management; Management science; Environmental resource management; Data science; Operations research; Engineering; Environmental science; Business; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004147747,0.00008627785,0.0002957592,0.00001416943,0.00003035587,0.000001732045,0.0001163255,0.00003012116,0.000077234],"category_scores_gemma":[0.0001295988,0.00007392565,0.00001704997,0.0001118668,0.0001990354,0.0000825984,0.00006670082,0.00004201737,0.00003362934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001459558,"about_ca_system_score_gemma":0.000006222978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007104758,"about_ca_topic_score_gemma":0.00001890842,"domain_scores_codex":[0.9989274,0.00006700698,0.000466586,0.0001730733,0.0002717962,0.00009410074],"domain_scores_gemma":[0.99908,0.0001481841,0.0004611932,0.0001864478,0.00006898352,0.00005518058],"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":[0.00007782301,0.0005721984,0.009918381,0.01429838,0.00007930237,9.173147e-7,0.001153979,0.00003097105,0.01542222,0.1167388,0.01608632,0.8256207],"study_design_scores_gemma":[0.001437484,0.002679073,0.3986697,0.00722218,0.0005973307,0.00001346711,0.0001202484,0.4925875,0.008006881,0.03356329,0.05420312,0.0008997543],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4450128,0.01117734,0.5260585,0.0001090869,0.00009719232,0.002567855,0.01099725,0.00002017446,0.003959835],"genre_scores_gemma":[0.977944,0.01266059,0.009223725,0.00003851502,0.00001134968,0.000018232,0.00008346535,0.000008274438,0.00001183777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.824721,"threshold_uncertainty_score":0.3014601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009032840692660797,"score_gpt":0.2576795427386559,"score_spread":0.2486467020459951,"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."}}