{"id":"W2963953671","doi":"10.3390/f10070607","title":"Optimizing Quality of Wood Pellets Made of Hardwood Processing Residues","year":2019,"lang":"en","type":"article","venue":"Forests","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Kruger (Canada); Natural Sciences and Engineering Research Council of Canada; Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"","keywords":"Pelletizing; Pellets; Sawdust; Pellet; Raw material; Pulp and paper industry; Materials science; Torrefaction; Water content; Moisture; Hardwood; Softwood; Wood processing; Compressive strength; Composite material; Waste management; Pyrolysis; Chemistry; Botany","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":[0.0001213122,0.0001132426,0.0002537205,0.00006648169,0.00001144095,0.000008040495,0.0002021473,0.00008674784,0.0001567673],"category_scores_gemma":[0.00005545223,0.0001088274,0.00006006903,0.0001712366,0.00004093759,0.000130858,0.00004990474,0.00008206248,0.00003113408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000197403,"about_ca_system_score_gemma":0.0000242952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008670258,"about_ca_topic_score_gemma":0.00001061446,"domain_scores_codex":[0.999157,0.00001344776,0.000320576,0.0001368228,0.0002030724,0.0001691382],"domain_scores_gemma":[0.9994988,0.00006365932,0.0001036543,0.0002154604,0.00007430002,0.00004410867],"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":[0.00003571971,0.00002597009,0.01203679,0.001957634,0.00002835539,6.898111e-7,0.000547902,0.0006578262,0.9829342,0.0001217821,0.0001744898,0.001478625],"study_design_scores_gemma":[0.0003418984,0.00002502979,0.01016142,0.0001970763,0.000008577778,8.873104e-7,0.0001123522,0.0004373954,0.9881905,0.000314379,0.00008041661,0.0001300253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961494,0.0008532699,0.000246611,0.00002071488,0.00007884521,0.000104,0.00001007814,0.0001147523,0.002422396],"genre_scores_gemma":[0.9976737,0.00001260732,0.002127763,0.000005648299,0.00001558339,0.000002928102,0.000005879039,0.00002406156,0.0001318421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005256327,"threshold_uncertainty_score":0.4437854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428282741215424,"score_gpt":0.2444550612650689,"score_spread":0.2301722338529146,"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."}}