{"id":"W1996624419","doi":"10.1007/s11069-012-0495-8","title":"Considerations for modeling burn probability across landscapes with steep environmental gradients: an example from the Columbia Mountains, Canada","year":2012,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Alberta; Ministry of Forests; Canadian Forest Service; Parks Canada; Mount Revelstoke National Park","funders":"Parks Canada","keywords":"Vegetation (pathology); Fire regime; Natural hazard; Environmental science; Fire history; Simulation modeling; Environmental resource management; Computer science; Meteorology; Physical geography; Geography; Climate change; Geology; Ecology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004647973,0.0001842981,0.0001927265,0.000004042462,0.0006988425,0.0001318524,0.0002224075,0.00007011653,0.0002856917],"category_scores_gemma":[0.00005048955,0.0001367586,0.0000430473,0.00006450552,0.00009894039,0.0005808254,0.00009914336,0.0002027197,0.00001123721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007935247,"about_ca_system_score_gemma":0.00005289414,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8929177,"about_ca_topic_score_gemma":0.9871356,"domain_scores_codex":[0.9982457,0.0001224164,0.0002272842,0.0003834551,0.0004652343,0.0005559346],"domain_scores_gemma":[0.9989949,0.0003188828,0.00008473828,0.0004221375,0.00001060645,0.0001686611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001637005,0.0003789171,0.9622503,0.00002459631,0.0001249061,0.000005722008,0.007929776,0.01103308,0.003095439,0.00005248201,0.007631456,0.00730961],"study_design_scores_gemma":[0.002869632,0.000316292,0.4115837,0.00003985834,0.0001053997,0.00005759055,0.004153985,0.5664427,0.00069356,0.0007034421,0.0119821,0.001051808],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974839,0.0001796876,0.0001720942,0.0001128354,0.0003859562,0.001102558,0.0004769748,0.00003833692,0.00004764391],"genre_scores_gemma":[0.9979715,0.000001454657,0.001104977,0.0003287901,0.0001615566,0.000158155,0.0001608599,0.00002367279,0.00008908506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5554096,"threshold_uncertainty_score":0.5576855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540728199449328,"score_gpt":0.2188242328715325,"score_spread":0.2034169508770392,"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."}}