{"id":"W2725711042","doi":"10.1016/j.scitotenv.2017.06.219","title":"Understanding fire drivers and relative impacts in different Chinese forest ecosystems","year":2017,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":131,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Asia-Pacific Network for Sustainable Forest Management and Rehabilitation; University of Asia Pacific; Fujian Agriculture and Forestry University; University of the Pacific","keywords":"Geography; Taiga; China; Ecosystem; Boreal ecosystem; Driving factors; Environmental science; Fire regime; Fire ecology; Forest ecology; Climate change; Boreal; Physical geography; Ecology; Environmental protection; Environmental resource management; Forestry","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":[0.000572965,0.0003445118,0.0002579966,0.002307071,0.00081309,0.0009929282,0.0003997599,0.000234039,0.001087169],"category_scores_gemma":[0.000734195,0.0002307913,0.0005511007,0.002241155,0.0004749321,0.00127111,0.0007337763,0.000289087,0.00005954721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002510549,"about_ca_system_score_gemma":0.001690244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1067413,"about_ca_topic_score_gemma":0.2291081,"domain_scores_codex":[0.9997347,0.00002824467,0.00002058623,0.00006039342,0.00004438343,0.0001117111],"domain_scores_gemma":[0.9995616,0.0001021916,0.0001170116,0.00002927826,0.000084274,0.0001056261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005585749,0.00002843279,0.9897076,0.00002581254,0.0001224094,0.0001037844,0.0008005938,0.0009745368,0.001064029,0.0008187672,0.00008332988,0.006214805],"study_design_scores_gemma":[0.000001976401,0.000007608641,0.9960789,0.000005530148,0.00006766384,0.00002255721,0.001189455,0.002007863,0.0001396359,0.0002776478,0.0001951174,0.000006138638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998924,0.000122655,0.0001225123,0.00003288571,0.000001619925,0.000003466796,0.00006919112,0.00000218904,0.0007214275],"genre_scores_gemma":[0.9995839,0.00009348991,0.00005364375,0.000007832899,0.00000184556,0.000002968432,0.00007743294,0.000001069542,0.000177923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1067413,"threshold_uncertainty_score":0.2122401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739217024632992,"score_gpt":0.2180099259205628,"score_spread":0.2006177556742329,"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."}}