{"id":"W4200224219","doi":"10.3390/en14248415","title":"The Contributions of Biomass Supply for Bioenergy in the Post-COVID-19 Recovery","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Bioeconomy and Sustainability Development","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"International Energy Agency","keywords":"Bioenergy; Biomass (ecology); Renewable energy; Environmental economics; Futures studies; Natural resource economics; Business; Investment (military); Environmental science; Delphi method; Energy supply; Environmental resource management; Economics; Engineering; Energy (signal processing); Ecology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004892212,0.00006210212,0.00009263365,0.000006391195,0.0003052707,0.00005236915,0.0002127258,0.00005185211,0.00004350857],"category_scores_gemma":[0.0006167612,0.00001742425,0.00008690177,0.0002306027,0.0001091941,0.00004544264,0.00004889822,0.00003587413,8.135798e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005028214,"about_ca_system_score_gemma":0.0001123922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008180304,"about_ca_topic_score_gemma":0.00688443,"domain_scores_codex":[0.9993136,0.0001220839,0.0001815587,0.0001342292,0.00006786567,0.0001806435],"domain_scores_gemma":[0.9983844,0.001352853,0.00004432587,0.00006451689,0.0001249782,0.0000289738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004759693,0.0005968733,0.02099119,0.0001102782,0.0001515989,0.00003063759,0.002512821,0.0006942905,0.280915,0.4439362,0.02274187,0.2268433],"study_design_scores_gemma":[0.0001736226,0.0001268161,0.05039407,0.000004259847,0.000005189574,0.000005310954,0.0121527,0.000008269247,0.05275941,0.015299,0.868969,0.0001023989],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9046118,0.0009029699,0.000007486686,0.093932,0.0001004549,0.0001306437,0.000130567,0.00001041024,0.000173655],"genre_scores_gemma":[0.9974037,0.0001863499,0.00005427275,0.001695334,0.00005013295,0.00009529775,0.0001208741,2.880607e-7,0.0003937393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8462271,"threshold_uncertainty_score":0.3841673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129270963753675,"score_gpt":0.2446089149866646,"score_spread":0.2316818186112971,"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."}}