{"id":"W4220883236","doi":"10.3390/fuels3010010","title":"Torrefaction and Densification of Wood Sawdust for Bioenergy Applications","year":2022,"lang":"en","type":"article","venue":"Fuels","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Saskatchewan Polytechnic; Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; BioFuelNet Canada","keywords":"Torrefaction; Sawdust; Pellets; Pulp and paper industry; Heat of combustion; Biochar; Materials science; Pyrolysis; Bioenergy; Waste management; Pelletizing; Furfural; Raw material; Steam explosion; Briquette; Pellet; Straw; Coal; Biofuel; Composite material; Chemistry; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002842548,0.00003567841,0.00004660762,0.00003367968,0.00004200306,0.000003348376,0.00004807133,0.00001868428,0.00006275223],"category_scores_gemma":[0.000005956091,0.00004070675,0.00001341,0.00009870789,0.00001139496,0.00003005323,0.0000169511,0.00002645717,0.000001629779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002554005,"about_ca_system_score_gemma":0.000004936316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002146553,"about_ca_topic_score_gemma":4.184758e-7,"domain_scores_codex":[0.9997543,0.000003372295,0.00007611037,0.00006975019,0.00004651686,0.00004997966],"domain_scores_gemma":[0.9998299,0.00003171061,0.00002095213,0.00007694167,0.00002235961,0.00001807698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005024242,0.0000101371,0.00003968961,0.00007724869,0.000009072483,2.677255e-8,0.00004138302,0.0001188097,0.9841385,0.001130755,0.000559602,0.01386974],"study_design_scores_gemma":[0.0001396172,0.00001803273,0.0001141212,0.000001797113,0.00001086852,0.000002084869,0.0001742233,0.0003894905,0.9363258,0.0008445396,0.06191294,0.00006643864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836072,0.0006855929,0.01444358,0.0001255352,0.0001350507,0.0002666948,0.00007945726,0.0001591124,0.0004978278],"genre_scores_gemma":[0.9990798,0.00002023927,0.0005319508,0.00001251699,0.00001934954,0.0002284625,0.00003295321,0.000008414304,0.00006626526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06135334,"threshold_uncertainty_score":0.1659973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058638680887867,"score_gpt":0.2073752338601465,"score_spread":0.1967888470512678,"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."}}