{"id":"W4313528441","doi":"10.1073/pnas.2212780120","title":"Warming-induced tree growth may help offset increasing disturbance across the Canadian boreal forest","year":2023,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Taiga; Climate change; Boreal; Environmental science; Disturbance (geology); Global warming; Carbon sequestration; Ecosystem; Offset (computer science); Productivity; Physical geography; Forest ecology; Ecology; Agroforestry; Geography; Forestry; Biology; Carbon dioxide","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005900956,0.0003863198,0.0002283956,0.0006544464,0.00113143,0.001155397,0.0004106535,0.0003205087,0.001184097],"category_scores_gemma":[0.00177346,0.0001387832,0.000534145,0.0008152495,0.0005088159,0.0007074589,0.0004597343,0.0004400308,0.0001493674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006320627,"about_ca_system_score_gemma":0.007225115,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9257177,"about_ca_topic_score_gemma":0.9641435,"domain_scores_codex":[0.9997638,0.00003056769,0.000009908445,0.00007494942,0.00004797018,0.00007290498],"domain_scores_gemma":[0.9996248,0.00005923388,0.00007100335,0.00003253375,0.0001562131,0.00005617449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001319585,0.00006530474,0.7978249,0.0001248606,0.0002743641,0.0001493028,0.0002476239,0.1246995,0.00893034,0.002557785,0.004151165,0.06084289],"study_design_scores_gemma":[0.00001058481,0.00002475666,0.9099711,0.00003000556,0.00007594279,0.00005236983,0.0004873425,0.08177316,0.001091824,0.001457347,0.004997497,0.00002799604],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798982,0.0006788491,0.006735062,0.001524222,0.00004194592,0.00002225739,0.002473658,0.0004009742,0.008224785],"genre_scores_gemma":[0.9954999,0.0002888174,0.002885395,0.0001018328,0.000008362822,0.000006371401,0.0007909663,0.00001702802,0.000401349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07428235,"threshold_uncertainty_score":0.1494396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549668826414919,"score_gpt":0.2665521687301001,"score_spread":0.2410554804659509,"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."}}