{"id":"W4362633791","doi":"10.1139/cjfr-2022-0207","title":"Response of forest productivity to changes in growth and fire regime due to climate change","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université du Québec en Abitibi-Témiscamingue; Université du Québec à Montréal; Natural Resources Canada; Canadian Forest Service","funders":"U.S. Forest Service; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Climate change; Taiga; Environmental science; Boreal; Context (archaeology); Fire regime; Productivity; Climate change scenario; Disturbance (geology); Climatology; Physical geography; Atmospheric sciences; Ecology; Geography; Ecosystem; Forestry; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0004886518,0.0003354438,0.000188677,0.0004414475,0.0004026811,0.0007991929,0.0004110638,0.0003824534,0.00119138],"category_scores_gemma":[0.001186709,0.0001579365,0.0004798412,0.000620275,0.0003042923,0.0002768647,0.0003657439,0.0004755364,0.0001311943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005517496,"about_ca_system_score_gemma":0.002229487,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6195074,"about_ca_topic_score_gemma":0.702727,"domain_scores_codex":[0.9997002,0.00004994371,0.00001238291,0.00006295277,0.00006466365,0.0001098121],"domain_scores_gemma":[0.9995528,0.00008488628,0.00008005684,0.00003035044,0.0001720227,0.0000798652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002856253,0.0001008343,0.9028184,0.00004149177,0.0002491827,0.0001394066,0.0001107668,0.08023464,0.005320812,0.0003235265,0.0007534224,0.009621959],"study_design_scores_gemma":[0.000007701089,0.00004062508,0.9756454,0.000004615851,0.00002035898,0.00003270636,0.0001355417,0.02288176,0.0004823838,0.00008647052,0.0006484485,0.00001404765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956753,0.0000939907,0.0008012802,0.0001412491,0.00001010757,0.00001705665,0.001587624,0.0000249893,0.001648382],"genre_scores_gemma":[0.9986007,0.00006344677,0.0002520639,0.00002375737,0.000003269751,0.000009136459,0.0007192587,0.000004339021,0.0003241293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6195074,"threshold_uncertainty_score":0.7654667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416879803672885,"score_gpt":0.2969190267371462,"score_spread":0.2527502287004174,"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."}}