{"id":"W2766915015","doi":"10.1111/1365-2745.12892","title":"Stand‐level drivers most important in determining boreal forest response to climate change","year":2017,"lang":"en","type":"article","venue":"Journal of Ecology","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Natural Resources Canada; Canadian Forest Service","funders":"Natural Resources Canada","keywords":"Climate change; Environmental science; Taiga; Boreal; Biomass (ecology); Ecosystem; Transect; Ecology; Vegetation (pathology); Disturbance (geology); Boreal ecosystem; Geography; Physical geography; Forestry; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002157409,0.0001134428,0.0003026002,0.0001266438,0.0001600553,0.00004244307,0.000481429,0.00009477774,0.0001088971],"category_scores_gemma":[0.0008009945,0.00009898109,0.00005278063,0.00006796201,0.00008758529,0.0003977865,0.0002586861,0.0001877794,0.00007768909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004399953,"about_ca_system_score_gemma":0.00003705725,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003416818,"about_ca_topic_score_gemma":0.02118554,"domain_scores_codex":[0.998607,0.0001578235,0.0004837364,0.0001585896,0.0001924967,0.0004003687],"domain_scores_gemma":[0.9986184,0.00022373,0.0006922365,0.0002705076,0.00001541775,0.0001797043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005196423,0.00004028754,0.9897424,0.00000556739,0.000006217713,0.001162582,0.0004725094,0.00004263606,0.002736303,0.000005997741,0.0003437042,0.004922159],"study_design_scores_gemma":[0.0008527865,0.0008339261,0.9953396,0.00005331235,0.00000774216,0.0002261419,0.00007609766,0.0009388549,0.0001223203,0.00003816385,0.001406873,0.0001041892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972812,0.000008989698,0.000009282918,0.0009701903,0.000671853,0.0002108017,0.00001267906,0.000004074287,0.0008309151],"genre_scores_gemma":[0.9990265,0.00003078172,0.0005951234,0.000203786,0.00009053805,0.00001007213,2.881109e-7,0.00001344378,0.00002950078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02084386,"threshold_uncertainty_score":0.9966753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896271933521125,"score_gpt":0.2809303985304165,"score_spread":0.2519676791952052,"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."}}