{"id":"W2475122297","doi":"10.3390/f7070139","title":"Detecting Local Drivers of Fire Cycle Heterogeneity in Boreal Forests: A Scale Issue","year":2016,"lang":"en","type":"article","venue":"Forests","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; Natural Resources Canada; Canadian Forest Service; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Chicoutimi; Université du Québec à Montréal","keywords":"Taiga; Boreal; Physical geography; Vegetation (pathology); Environmental science; Ecosystem; Spatial heterogeneity; Fire regime; Geography; Akaike information criterion; Scale (ratio); Ecology; Spatial ecology; Terrain; Forestry; Cartography; Biology","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":[0.0002851921,0.0001406336,0.0002020268,0.00003816873,0.00004738641,0.0000076948,0.0002610321,0.00009674843,0.0002190241],"category_scores_gemma":[0.00007345597,0.0001049977,0.0000673631,0.0002026083,0.0002149852,0.0002383721,0.0001779553,0.00007322805,0.0004309322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003044046,"about_ca_system_score_gemma":0.00000882494,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005493932,"about_ca_topic_score_gemma":0.1425431,"domain_scores_codex":[0.9986939,0.00007591843,0.0002622266,0.0003166874,0.000281862,0.0003693576],"domain_scores_gemma":[0.9993486,0.0001161483,0.0001068878,0.0003151321,0.000005710849,0.0001074605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002343527,0.00004280947,0.9196337,0.00001945241,0.000004052775,0.00001425682,0.0001663454,0.0004095525,0.001881117,0.000002617761,0.0002471624,0.07755548],"study_design_scores_gemma":[0.0006003504,0.0001632683,0.9639291,0.000118336,0.000003941837,0.00001043108,0.0000276259,0.01128723,0.0231379,0.000158133,0.0004166802,0.0001470541],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977178,0.00001579249,0.0008269703,0.00006898531,0.0001346964,0.0002688699,0.00001335057,0.00003962619,0.0009139286],"genre_scores_gemma":[0.9996657,0.000002499982,0.0001666418,0.00001694367,0.00003587659,0.00002380033,0.000001455219,0.00001961518,0.00006749455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1370492,"threshold_uncertainty_score":0.8731033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004665285562140424,"score_gpt":0.2169086629747053,"score_spread":0.2122433774125649,"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."}}