{"id":"W1971528452","doi":"10.2307/3315984","title":"Reconstructing the history of forest fire frequency: Identifying hazard rate change points using the bayes information criterion","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bayes' theorem; Hazard; Statistics; Fire history; Selection (genetic algorithm); Function (biology); Change detection; Constant (computer programming); Computer science; Identification (biology); Climate change; Mathematics; Artificial intelligence; Bayesian probability; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005098981,0.0004113733,0.0007333422,0.002615987,0.000507989,0.001200001,0.0009278193,0.000695775,0.0007751671],"category_scores_gemma":[0.01742032,0.0005201636,0.0006255506,0.00120129,0.0008054821,0.0009337185,0.0007427832,0.0008023729,0.000135085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00106422,"about_ca_system_score_gemma":0.001434605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05255842,"about_ca_topic_score_gemma":0.03761039,"domain_scores_codex":[0.9990492,0.000408281,0.00006023965,0.0001946857,0.0002024843,0.00008501312],"domain_scores_gemma":[0.9888197,0.009042953,0.0008329586,0.0006257758,0.0005001213,0.0001785323],"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":[0.0003403147,0.00009595876,0.1471786,0.000128455,0.0002820158,0.0003402199,0.000368893,0.6917521,0.004217138,0.02506819,0.001745467,0.1284827],"study_design_scores_gemma":[0.0000144113,0.00002106356,0.01469781,0.00002385037,0.00002541769,0.0000527948,0.00004178648,0.9651945,0.000943024,0.01849208,0.0004674317,0.00002569666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3488948,0.0004071154,0.6484011,0.0002940256,0.00001724878,0.00003759965,0.0008558844,0.0002351189,0.0008570643],"genre_scores_gemma":[0.9204062,0.0001341256,0.07813594,0.00002839849,0.00002066525,0.00004066926,0.0006866624,0.00003260182,0.0005148284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05255842,"threshold_uncertainty_score":0.104505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02931802792167048,"score_gpt":0.2161548385637969,"score_spread":0.1868368106421265,"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."}}