{"id":"W2892126241","doi":"10.1186/s13021-018-0100-x","title":"A systems approach to assess climate change mitigation options in landscapes of the United States forest sector","year":2018,"lang":"en","type":"article","venue":"Carbon Balance and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; U.S. Forest Service; Comisión Nacional Forestal; Commission for Environmental Cooperation","keywords":"Environmental science; Greenhouse gas; Baseline (sea); Climate change; Climate change mitigation; Scenario analysis; Carbon sink; Land use, land-use change and forestry; Bioenergy; Carbon sequestration; Primary production; Land use; Ecosystem; Forest management; Ecosystem services; Agroforestry; Business; Biofuel; Ecology; Carbon dioxide","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003769075,0.001025207,0.000468297,0.002920734,0.0009715335,0.0023528,0.001028995,0.001191839,0.005456241],"category_scores_gemma":[0.004532204,0.0003826139,0.001522406,0.002191983,0.0007793873,0.001719359,0.001805748,0.0007016276,0.0001709857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007011991,"about_ca_system_score_gemma":0.002604642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03228048,"about_ca_topic_score_gemma":0.03852639,"domain_scores_codex":[0.9981742,0.001242182,0.00007243258,0.0002274939,0.0001708102,0.0001129256],"domain_scores_gemma":[0.9979004,0.001328355,0.0002862367,0.00007852743,0.000283382,0.0001232099],"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":[0.0001471193,0.0003311312,0.06334008,0.000238958,0.0007192252,0.0001617797,0.0002962339,0.8849782,0.001157799,0.02227863,0.00157711,0.02477374],"study_design_scores_gemma":[0.00005691299,0.0003953138,0.0205859,0.00006986798,0.000181229,0.00004312939,0.00112637,0.9493297,0.0004712234,0.02384222,0.003859757,0.00003834775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838383,0.001157979,0.1711336,0.00139676,0.0001075566,0.002570302,0.006886375,0.0003955652,0.03251357],"genre_scores_gemma":[0.942419,0.0002631615,0.05370025,0.0001230372,0.00002064429,0.001161274,0.001153164,0.000018084,0.001141433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03228048,"threshold_uncertainty_score":0.06418514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370296214704674,"score_gpt":0.2384451794564107,"score_spread":0.214742217309364,"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."}}