{"id":"W1557427700","doi":"10.1002/grl.50504","title":"Paleofire reconstruction based on an ensemble‐member strategy applied to sedimentary charcoal","year":2013,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université du Québec en Abitibi-Témiscamingue; Université du Québec à Montréal","funders":"Canadian Forest Service; Centre National de la Recherche Scientifique; Fonds de recherche du Québec – Nature et technologies; Max-Planck-Gesellschaft; Agence Nationale de la Recherche; Biodiversa+; Université du Québec à Chicoutimi; Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Charcoal; Char; Smoothing; Fire regime; Environmental science; Biome; Taiga; Sedimentary rock; Computer science; Geology; Statistics; Mathematics; Coal; Paleontology; Chemistry; Archaeology; Forestry; Geography; Ecology","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":[0.002046434,0.000479448,0.0007077074,0.001055022,0.0006548046,0.000996554,0.0008813656,0.000865322,0.0008876874],"category_scores_gemma":[0.003866354,0.0003491913,0.0008179013,0.0007245209,0.0003416575,0.0006456929,0.0007418416,0.0006205409,0.0002675397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003724761,"about_ca_system_score_gemma":0.0007335222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007831161,"about_ca_topic_score_gemma":0.007070384,"domain_scores_codex":[0.9997436,0.00009484328,0.0000137013,0.00005671145,0.00005203495,0.00003895562],"domain_scores_gemma":[0.9981341,0.0007456538,0.0001417371,0.0003296474,0.0005281498,0.0001207694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002133773,0.00009307534,0.01687287,0.00003009428,0.0001995678,0.0001381499,0.0002166483,0.8441489,0.01046685,0.004144384,0.0005912072,0.1228849],"study_design_scores_gemma":[0.000001999985,0.000005054145,0.0008099498,0.000001806232,0.00000520511,0.000007044247,0.000006320001,0.9980118,0.0006381606,0.0004277967,0.00008062275,0.000004289299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2390782,0.00006441692,0.7591618,0.00006846155,0.00002094273,0.00002355369,0.0001175559,0.0006728215,0.0007922944],"genre_scores_gemma":[0.7816344,0.00005368851,0.2169907,0.00003342012,0.00002952813,0.00004689043,0.0004812256,0.0001222322,0.0006078674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007831161,"threshold_uncertainty_score":0.01557118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413266802728866,"score_gpt":0.2943387104066323,"score_spread":0.2530120301337457,"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."}}