{"id":"W2300962069","doi":"10.3390/rs8030252","title":"Assessing Earthquake-Induced Tree Mortality in Temperate Forest Ecosystems: A Case Study from Wenchuan, China","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Biological and Environmental Research; Office of Science; Chinese Academy of Sciences; National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Environmental science; China; Forest ecology; Biomass (ecology); Disturbance (geology); Epicenter; Physical geography; Ecosystem; Geography; Ecology; Geology; Seismology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0007713744,0.000509789,0.0003480157,0.001288298,0.0008673581,0.0004712545,0.000701134,0.0005873568,0.0002744848],"category_scores_gemma":[0.0008340716,0.0002032269,0.0005568821,0.001315202,0.0004924301,0.0004053757,0.0006039597,0.0002051678,0.000039191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001687701,"about_ca_system_score_gemma":0.0009514372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07767013,"about_ca_topic_score_gemma":0.1340161,"domain_scores_codex":[0.9997709,0.00006216876,0.00001926178,0.000037791,0.00004296263,0.00006691116],"domain_scores_gemma":[0.9995327,0.0001547365,0.00009160896,0.00004366767,0.00009164968,0.00008566757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001700996,0.000575,0.9353211,0.00009485047,0.0001625084,0.009818953,0.001562162,0.03279627,0.003861611,0.0004139442,0.0002715207,0.0149519],"study_design_scores_gemma":[0.00003123352,0.0003539513,0.9199509,0.00001718076,0.0001068174,0.0007594691,0.004509425,0.07216024,0.001390818,0.0003596053,0.0003201807,0.00004021337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997357,0.00001457786,0.0001014191,0.00001157485,4.627778e-7,0.000008334367,0.00002932405,0.000002177596,0.00009639664],"genre_scores_gemma":[0.9994751,0.00004094165,0.0003140781,0.000006845175,0.00000198669,0.000006284062,0.00008162238,0.000001048632,0.00007207053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07767013,"threshold_uncertainty_score":0.1544361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628814019020525,"score_gpt":0.2773022632698617,"score_spread":0.2510141230796565,"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."}}