{"id":"W3216491865","doi":"10.21203/rs.3.rs-1031503/v1","title":"Prediction of stand carbon (C) storage and net primary production (NPP) of secondary forests in subtropical China: the effect of climate change and its contribution to carbon neutrality in 2060","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Key Research and Development Program of China; National Forestry and Grassland Administration","keywords":"Evergreen; Primary production; Deciduous; Environmental science; Carbon sequestration; Climate change; Subtropics; Forestry; Agroforestry; Tropical and subtropical moist broadleaf forests; Carbon neutrality; Ecology; Ecosystem; Geography; Greenhouse gas; Biology; Carbon dioxide","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":[0.0004920733,0.0008369456,0.0004068088,0.0003422964,0.0004081204,0.0005748281,0.0006270288,0.0005426009,0.0007371584],"category_scores_gemma":[0.0003500796,0.0003613669,0.0008765102,0.0002617068,0.0004410515,0.0003814881,0.0003104598,0.0003452038,0.00009690809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523567,"about_ca_system_score_gemma":0.001120353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08197206,"about_ca_topic_score_gemma":0.04911082,"domain_scores_codex":[0.9999152,0.00001482986,0.000005262058,0.00003075023,0.00001015394,0.0000238197],"domain_scores_gemma":[0.9997676,0.00007045495,0.00002833109,0.00001681646,0.00004447128,0.00007230975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002282625,0.0001002199,0.1288616,0.00003329495,0.0001062897,0.0001342383,0.00004799999,0.8619483,0.004471097,0.0001683199,0.0001767716,0.003723628],"study_design_scores_gemma":[0.00002093899,0.00006368441,0.03554706,0.000002594672,0.00002514451,0.00001179298,0.00002323638,0.9633325,0.0008410179,0.000069537,0.00005345577,0.000009019544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991874,0.00002096701,0.000428107,0.00001921471,0.000003269757,0.000003090434,0.0001060852,0.00002492488,0.0002068675],"genre_scores_gemma":[0.9995188,0.00001009966,0.0002749737,0.00000395151,0.000001163693,0.000003266072,0.0001122098,0.00000212751,0.0000735513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08197206,"threshold_uncertainty_score":0.1629899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02217996425555736,"score_gpt":0.292584453658457,"score_spread":0.2704044894028997,"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."}}