{"id":"W4293843068","doi":"10.1139/as-2021-0042","title":"Combining community observations and remote sensing to examine the effects of roads on wildfires in the East Siberian boreal forest","year":2022,"lang":"en","type":"article","venue":"Arctic Science","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"George Washington University; National Science Foundation","keywords":"Boreal; Taiga; Geography; Subsistence agriculture; Fire regime; Environmental resource management; Wildfire suppression; Environmental science; Physical geography; Indigenous; Ecology; Forestry; Firefighting; Agriculture; Cartography; Ecosystem","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004039545,0.00008526908,0.0001002486,0.00005285964,0.001143339,0.00004977489,0.0007172062,0.00001041219,0.000004926547],"category_scores_gemma":[0.001555321,0.00005618376,0.00001549151,0.001240672,0.000563815,0.0001402187,0.0005982468,0.0002716069,0.000003571433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001959052,"about_ca_system_score_gemma":0.00002288627,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03632458,"about_ca_topic_score_gemma":0.01275525,"domain_scores_codex":[0.998038,0.0006793361,0.0001512723,0.0001945472,0.0006975317,0.0002392993],"domain_scores_gemma":[0.9976502,0.001678398,0.00007553655,0.0005347137,0.000009709845,0.0000514422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005194701,0.0002790364,0.7325347,0.00009323197,0.000008456169,0.0000293377,0.08748576,0.005361694,0.06947906,0.0008968003,0.0003502573,0.1034297],"study_design_scores_gemma":[0.0001521723,0.0003074865,0.9676776,0.00006052507,0.000003735359,0.00002106766,0.00179493,0.02916552,0.0002724565,0.000358826,0.000119465,0.00006624007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965773,0.0000059159,0.00003203995,0.001662584,0.000155618,0.0004806784,0.000002126667,0.00001004181,0.001073707],"genre_scores_gemma":[0.998607,6.828044e-7,0.0003692648,0.0009917291,0.000007410285,0.000005539635,9.258129e-7,0.000005573492,0.00001184095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2351429,"threshold_uncertainty_score":0.9700926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037062489984432,"score_gpt":0.232142057300611,"score_spread":0.2117714324007667,"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."}}