{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002222293,0.0003699494,0.0005284413,0.000873168,0.0006827504,0.001137739,0.000475407,0.0003232155,0.0008190212],"category_scores_gemma":[0.005244189,0.0002015367,0.0006388659,0.0007271119,0.0006703393,0.001069803,0.0005238131,0.0003907785,0.00009069699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003134024,"about_ca_system_score_gemma":0.0003025673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01139407,"about_ca_topic_score_gemma":0.03792874,"domain_scores_codex":[0.9993991,0.0002359558,0.00004360799,0.0001877508,0.00006362126,0.00007005197],"domain_scores_gemma":[0.9946839,0.00306548,0.001266408,0.00045043,0.00017363,0.0003602164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002821598,0.00001697129,0.9941618,0.00001048868,0.00007581294,0.00002588014,0.0000995731,0.000800585,0.0007441337,0.00005880715,0.00002826149,0.003949523],"study_design_scores_gemma":[0.000001841172,0.00002544058,0.9952472,0.000004741301,0.00002161225,0.00005767738,0.0001725767,0.004121625,0.0001144881,0.0001555383,0.00007125922,0.000005932864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977944,0.0001884369,0.0015451,0.00002990136,0.00000269953,0.000006715616,0.00008554926,0.00001500236,0.0003322522],"genre_scores_gemma":[0.9994282,0.00001764832,0.0004242456,0.000005178883,0.000003762299,0.000002615947,0.00008084065,0.000002913889,0.0000345107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01139407,"threshold_uncertainty_score":0.02265555,"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."}}