{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008312569,0.0007666686,0.0005084773,0.0008949874,0.0002405043,0.001245198,0.0003608722,0.0003595268,0.0009489915],"category_scores_gemma":[0.001249243,0.000367998,0.0005467039,0.0007449253,0.0001796434,0.0006798429,0.0004776419,0.000437902,0.0003746708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00028666,"about_ca_system_score_gemma":0.0002307398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007556994,"about_ca_topic_score_gemma":0.002242319,"domain_scores_codex":[0.9992454,0.00008161826,0.00007881285,0.0001206426,0.0003631485,0.0001102544],"domain_scores_gemma":[0.9993388,0.00009605689,0.0002404999,0.000045955,0.0002123819,0.00006621509],"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":[0.000419714,0.0001186853,0.004516624,0.0001747372,0.00004316244,0.0001306153,0.00002507113,0.0009764387,0.9801648,0.00004965831,0.00003530736,0.01334523],"study_design_scores_gemma":[0.00003212136,0.001260919,0.03330873,0.00004771239,0.0001197256,0.0001682676,0.00008233669,0.002146799,0.9616832,0.00004412784,0.001092025,0.00001413888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920953,0.000954565,0.005672472,0.00001949462,0.00001482121,0.00005868408,0.0001577167,0.00007247885,0.0009545067],"genre_scores_gemma":[0.9848852,0.0008141327,0.01270537,0.00002836717,0.0000074584,0.00003115144,0.0004534335,0.0001187106,0.0009561905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001245198,"threshold_uncertainty_score":0.0043962,"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."}}