{"id":"W3138750112","doi":"10.1002/eap.2327","title":"Opportunities for forest sector emissions reductions: a state‐level analysis","year":2021,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"U.S. Forest Service","keywords":"Business as usual; Environmental science; Greenhouse gas; Baseline (sea); Scenario analysis; Bioenergy; Carbon sequestration; Renewable energy; Carbon sink; Fossil fuel; Climate change; Climate change mitigation; Carbon neutrality; Environmental protection; Ecology; Engineering; Business; Carbon dioxide; Waste management; 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":[0.001318507,0.000253201,0.0003360095,0.0006613182,0.0004691683,0.001193318,0.0005748437,0.0004501411,0.002881185],"category_scores_gemma":[0.001579702,0.0002136833,0.0008081783,0.001480469,0.0002440152,0.0007994279,0.000406104,0.0004157564,0.0001288066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004365051,"about_ca_system_score_gemma":0.002473702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1622065,"about_ca_topic_score_gemma":0.2344553,"domain_scores_codex":[0.9994683,0.0002013196,0.00001488431,0.00007605278,0.0001031618,0.0001362812],"domain_scores_gemma":[0.9990067,0.0004470495,0.0001575593,0.00005006538,0.0002977716,0.00004089106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002296232,0.0001276499,0.1476391,0.0001029856,0.0004415509,0.0002425406,0.0001069721,0.8174074,0.00268167,0.009953402,0.002098469,0.01896869],"study_design_scores_gemma":[0.0000407983,0.0001896155,0.1307492,0.00004832454,0.0003382522,0.00004848061,0.0003093669,0.8533366,0.002280655,0.004834771,0.007782487,0.0000413982],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770811,0.0002736254,0.007143666,0.000585928,0.00001463251,0.0000824692,0.0041868,0.00006831782,0.01056339],"genre_scores_gemma":[0.9961288,0.0000985365,0.00144749,0.00005277177,0.000004657507,0.00003403061,0.001035126,0.00000725997,0.001191231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1622065,"threshold_uncertainty_score":0.3225248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026508113038967,"score_gpt":0.2962351586296177,"score_spread":0.193584347325721,"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."}}