{"id":"W4293238329","doi":"10.3390/en15093179","title":"Impact of the COVID-19 Pandemic on Biomass Supply Chains: The Case of the Canadian Wood Pellet Industry","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Food Waste Reduction and Sustainability","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Pandemic; Business; Supply chain; Agricultural economics; Pellet; Production (economics); Coronavirus disease 2019 (COVID-19); Wood industry; Economic impact analysis; Supply and demand; Biomass (ecology); Economics; Geography; Forestry; Marketing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003628885,0.00008272917,0.00009471082,0.00001236805,0.0008769268,0.00001616565,0.0004177839,0.00007105535,0.0003966121],"category_scores_gemma":[0.000198222,0.000019294,0.0001742336,0.0005250949,0.000271779,0.00001952854,0.0001453652,0.0002800121,2.14867e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003024134,"about_ca_system_score_gemma":0.0002946346,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4422531,"about_ca_topic_score_gemma":0.3728998,"domain_scores_codex":[0.9990163,0.0003842407,0.000133839,0.0001280872,0.0001601416,0.0001773182],"domain_scores_gemma":[0.9994774,0.0001586471,0.00009357489,0.0001521704,0.0000350482,0.00008318792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001956109,0.0004580492,0.829019,0.00005140472,0.0002051078,0.00007470097,0.008137898,0.05198714,0.0312954,0.009863638,0.03391923,0.03479287],"study_design_scores_gemma":[0.0003523574,0.000846141,0.7664424,0.000008964151,0.00003168859,0.0007662899,0.1677497,0.0001188019,0.002274356,0.001491618,0.05963481,0.0002828608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846094,0.00007059435,6.114897e-9,0.01445765,0.0001506008,0.0001651929,0.0002186386,0.000009935216,0.0003179249],"genre_scores_gemma":[0.9988478,0.000003822309,2.287176e-7,0.0004359242,0.00004785617,0.00001612361,0.000004205974,6.363558e-7,0.0006433577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1596118,"threshold_uncertainty_score":0.67447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03535316423092806,"score_gpt":0.2719208527581295,"score_spread":0.2365676885272015,"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."}}