{"id":"W2802396268","doi":"10.3390/f9050227","title":"Responses of the Carbon Storage and Sequestration Potential of Forest Vegetation to Temperature Increases in Yunnan Province, SW China","year":2018,"lang":"en","type":"article","venue":"Forests","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"National Key Research and Development Program of China; Yunnan University; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Carbon sequestration; Environmental science; Vegetation (pathology); Evergreen; Evergreen forest; Climate change; Forestry; Agroforestry; Ecology; Carbon dioxide; Geography","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":[0.0002613839,0.0002502989,0.0001635858,0.0003681254,0.0002895683,0.000379409,0.0001878653,0.0001787491,0.0004029035],"category_scores_gemma":[0.0005477609,0.0001432841,0.0002901122,0.0005928286,0.0002060663,0.0002296928,0.0002419441,0.000164422,0.00005253136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008450932,"about_ca_system_score_gemma":0.0007782227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1102372,"about_ca_topic_score_gemma":0.1262833,"domain_scores_codex":[0.999873,0.00001915957,0.00001218651,0.0000337097,0.00002532539,0.00003661471],"domain_scores_gemma":[0.9997699,0.0000406767,0.0000515436,0.00001533182,0.00008204962,0.00004062917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000116769,0.00003355273,0.9836083,0.00003224264,0.00007831723,0.0002382463,0.0004327999,0.003984485,0.005803034,0.00007561642,0.0001770584,0.005419594],"study_design_scores_gemma":[0.000004334607,0.00001617894,0.9956828,0.000002969859,0.00001185228,0.00003045643,0.0003127145,0.00351487,0.0002336941,0.00003597196,0.0001497672,0.000004344689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996531,0.00003759292,0.00004222261,0.00001783334,0.00000142061,0.000001664277,0.00008677783,0.000002507929,0.0001568116],"genre_scores_gemma":[0.9996743,0.00003291988,0.00003800078,0.000005043231,0.000001234354,0.000003108357,0.0001512098,9.48112e-7,0.00009321738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102372,"threshold_uncertainty_score":0.2191911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004322158552194273,"score_gpt":0.2209011428577399,"score_spread":0.2165789843055456,"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."}}