{"id":"W2982511501","doi":"10.1126/sciadv.aax2546","title":"Degradation and forgone removals increase the carbon impact of intact forest loss by 626%","year":2019,"lang":"en","type":"article","venue":"Science Advances","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"John D. and Catherine T. MacArthur Foundation","keywords":"Defaunation; Carbon sequestration; Deforestation (computer science); Environmental science; Greenhouse gas; Carbon fibers; Carbon accounting; Forest degradation; Climate change; Logging; Atmospheric carbon cycle; Global warming; Natural resource economics; Environmental protection; Agroforestry; Land use; Forestry; Carbon dioxide; Ecology; Geography; Land degradation; Chemistry; Biology; Economics","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":[0.00076638,0.0005573016,0.0003466812,0.0005916894,0.0006488396,0.001317948,0.0005631348,0.0006541045,0.00884613],"category_scores_gemma":[0.001954051,0.0001375237,0.001214446,0.0008957747,0.001040829,0.001651725,0.001696294,0.0007983772,0.0007120575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547567,"about_ca_system_score_gemma":0.001540208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01843687,"about_ca_topic_score_gemma":0.03915183,"domain_scores_codex":[0.9990182,0.0001215742,0.00004324834,0.0001519136,0.0003368726,0.0003281128],"domain_scores_gemma":[0.9991599,0.0001205996,0.0003366258,0.0001010693,0.0001600154,0.00012179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001577714,0.0005397883,0.6374608,0.001052253,0.001401168,0.002015512,0.0007700694,0.04037932,0.04278622,0.01654604,0.01457684,0.2408942],"study_design_scores_gemma":[0.00002821459,0.0003306718,0.9329406,0.0001583342,0.0003552223,0.001599045,0.001129892,0.01357352,0.01083739,0.005897928,0.03309641,0.00005274306],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700508,0.00189195,0.002620238,0.001513018,0.0001495853,0.00003241351,0.002762591,0.0001274707,0.02085188],"genre_scores_gemma":[0.996709,0.0004257881,0.000658608,0.0001886573,0.00002122664,0.000008792817,0.0008530035,0.00001333008,0.001121489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01843687,"threshold_uncertainty_score":0.03665912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004802582174817304,"score_gpt":0.2589488485423807,"score_spread":0.2541462663675634,"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."}}