{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001312771,0.0002289382,0.0003229014,0.00003669563,0.0001816779,0.00003193181,0.0002069583,0.0001232647,0.002184159],"category_scores_gemma":[0.0001441468,0.000224489,0.00008383562,0.0005081182,0.000141445,0.0003315737,0.0002248072,0.00013704,0.0008268237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003064717,"about_ca_system_score_gemma":0.00003076409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002609659,"about_ca_topic_score_gemma":0.004402782,"domain_scores_codex":[0.9978029,0.0003428543,0.0003509934,0.0005514395,0.000449495,0.0005022825],"domain_scores_gemma":[0.998957,0.000123683,0.0002439673,0.0004349645,0.00001987821,0.0002205522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000131966,0.0004192802,0.2193675,0.0001726362,0.00002578731,0.00002875177,0.0007536487,0.0001820708,0.1959223,0.00001327358,0.0008610877,0.5821216],"study_design_scores_gemma":[0.0006101096,0.000852565,0.8923869,0.0001017516,0.00002750825,0.000007770226,0.0001047315,0.03601298,0.06912554,0.00001724898,0.0003585181,0.00039435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848586,0.000007457261,0.001009707,0.00005813083,0.0003201769,0.0007475736,0.00003592057,0.00007055993,0.01289193],"genre_scores_gemma":[0.9937074,3.190603e-7,0.005795768,0.0002831635,0.0001201672,0.00003286105,0.000002344632,0.00003463978,0.00002331049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6730194,"threshold_uncertainty_score":0.9999511,"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."}}