{"id":"W4313828115","doi":"10.1016/j.jenvman.2022.117197","title":"Forestry based products as climate change solution: Integrating life cycle assessment with techno-economic analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Natural Resources Canada; École Nationale d'Administration Publique; National Research Council Canada; Concordia University; Université du Québec à Montréal","funders":"Office of Energy Research and Development; Natural Resources Canada","keywords":"Biorefinery; Life-cycle assessment; Raw material; Biomass (ecology); Environmental science; Cost of electricity by source; Bioenergy; Pellets; Sugar; Greenhouse gas; Biofuel; Carbon footprint; Pulp and paper industry; Wood processing; Waste management; Forestry; Environmental engineering; Production (economics); Electricity generation; Engineering; Economics; Agronomy","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":[0.001781327,0.0004851649,0.0004103978,0.002209884,0.0004006283,0.002860214,0.0006472197,0.0009189787,0.003669652],"category_scores_gemma":[0.002963422,0.0002738893,0.0005545552,0.002382668,0.000518161,0.002071481,0.000621056,0.0005264984,0.0002954142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00282891,"about_ca_system_score_gemma":0.001802258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01423063,"about_ca_topic_score_gemma":0.02396505,"domain_scores_codex":[0.9992624,0.000355942,0.00001939506,0.00003815278,0.0002790387,0.00004506344],"domain_scores_gemma":[0.9989059,0.0005995824,0.00009105381,0.00005262457,0.0003075133,0.00004329411],"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":[0.000139787,0.0002756771,0.01487104,0.0001399592,0.0001105089,0.0001082853,0.00006378521,0.8581219,0.002075154,0.02337452,0.00144279,0.09927664],"study_design_scores_gemma":[0.000007876334,0.00007312848,0.004579981,0.00003567283,0.00004600242,0.0000227344,0.0001482243,0.9756638,0.001464548,0.01509428,0.002842621,0.00002111425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.629643,0.002777819,0.2623339,0.002092404,0.0001336266,0.000627119,0.001781045,0.0004767429,0.1001343],"genre_scores_gemma":[0.9671962,0.0006943466,0.02908616,0.00003364794,0.00001368833,0.00007457223,0.0002846613,0.00004437175,0.002572248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01423063,"threshold_uncertainty_score":0.02829564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00937707656100231,"score_gpt":0.248390282208447,"score_spread":0.2390132056474447,"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."}}