{"id":"W2788559162","doi":"10.1002/ecs2.2108","title":"Tree vulnerability to climate change: improving exposure‐based assessments using traits as indicators of sensitivity","year":2018,"lang":"en","type":"article","venue":"Ecosphere","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Vulnerability (computing); Boreal; Climate change; Adaptive capacity; Geography; Habitat; Environmental science; Biomass (ecology); Taiga; Vulnerability assessment; Global change; Ecology; Environmental resource management; Physical geography; Forestry; Psychological resilience; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002015799,0.0007512334,0.0004823913,0.003075074,0.0004548515,0.001663338,0.0005692944,0.0004385445,0.0008827557],"category_scores_gemma":[0.004163035,0.0002579759,0.0005101704,0.002427428,0.0002847429,0.00095722,0.00137164,0.0005035112,0.0001247487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009813808,"about_ca_system_score_gemma":0.0008539195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1175806,"about_ca_topic_score_gemma":0.2132436,"domain_scores_codex":[0.9992418,0.0002610806,0.00006321678,0.0001597291,0.0002009,0.00007313564],"domain_scores_gemma":[0.9975643,0.0007900301,0.0006191403,0.0002105091,0.0006358176,0.0001802012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001207833,0.00006831764,0.9217454,0.0001273092,0.0004451493,0.00007472355,0.0004305618,0.03555243,0.005846597,0.0004445982,0.0003562185,0.03478798],"study_design_scores_gemma":[0.000007971181,0.0001002265,0.8981994,0.00004026657,0.0001061212,0.00005699955,0.0005708438,0.09632017,0.002102106,0.0015338,0.0009012767,0.00006095758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615891,0.0002732234,0.03169285,0.00008131517,0.000007744685,0.0000782063,0.002984932,0.0002402,0.003052392],"genre_scores_gemma":[0.9871251,0.00008180127,0.01185119,0.00002322305,0.00000428422,0.00003786612,0.0006923528,0.00001329183,0.0001710318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1175806,"threshold_uncertainty_score":0.2337925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01807116377604854,"score_gpt":0.2872749587422923,"score_spread":0.2692037949662437,"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."}}