{"id":"W3091718043","doi":"10.1186/s13021-020-00155-2","title":"Climate change mitigation in British Columbia’s forest sector: GHG reductions, costs, and environmental impacts","year":2020,"lang":"en","type":"article","venue":"Carbon Balance and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Natural Resources Canada; Canadian Forest Service; Government of Canada; U.S. Forest Service; Pacific Institute for Climate Solutions","keywords":"Greenhouse gas; Environmental science; Fossil fuel; Bioenergy; Business as usual; Climate change mitigation; Natural resource economics; Portfolio; Renewable energy; Carbon sequestration; Environmental protection; Agroforestry; Business; Ecology; Economics; Engineering; Waste management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008852267,0.0000983987,0.0001189043,0.00002265005,0.00007434726,0.000132151,0.00007962433,0.00003497997,0.0001958595],"category_scores_gemma":[0.000003348464,0.0001504799,0.00001769737,0.0001306329,0.0001177121,0.0002253804,0.0002710537,0.0000663582,0.00003413624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037878,"about_ca_system_score_gemma":7.943054e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006328552,"about_ca_topic_score_gemma":0.01376037,"domain_scores_codex":[0.9990699,0.00002245296,0.0001462165,0.00033421,0.0001440913,0.0002831655],"domain_scores_gemma":[0.9997123,0.000004298271,0.00004955922,0.00010901,6.85673e-7,0.0001240799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007558613,0.00003078314,0.977355,0.00008242501,0.000009150542,0.00003708083,0.0002806936,0.0000139243,0.00009308805,0.00008797183,0.007112607,0.0148897],"study_design_scores_gemma":[0.0006725418,0.00007430753,0.9772446,0.00006021906,0.00002341222,0.000005516573,0.0002867095,0.004807753,0.000004399412,0.00007993131,0.01653393,0.0002066389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870523,0.0003628257,0.000001085851,0.0008042539,0.00006550979,0.0006025356,0.0000170429,0.00003471471,0.01105968],"genre_scores_gemma":[0.9897057,0.008411762,0.0000595878,0.001181014,0.00008179238,0.00009054822,0.00004345455,0.00001347366,0.0004126808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01468307,"threshold_uncertainty_score":0.9566921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008279758973176118,"score_gpt":0.1939158098201598,"score_spread":0.1856360508469836,"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."}}