{"id":"W4401173819","doi":"10.1115/1.4066104","title":"Maximizing Waste-to-Energy Potential: Optimizing Batch Torrefaction Reactor of Refuse-Derived Fuel for Efficient Gasification","year":2024,"lang":"en","type":"article","venue":"Journal of energy resources technology.","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Enerkem (Canada); Université de Sherbrooke","funders":"","keywords":"Torrefaction; Heat of combustion; Combustion; Refuse-derived fuel; Waste management; Materials science; Pulp and paper industry; Wood gas generator; Inert gas; Ignition system; Environmental science; Coal; Chemistry; Incineration; Pyrolysis; Organic chemistry; Composite material; Engineering","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":[0.0001915012,0.0002005945,0.0003419333,0.0009776051,0.00003984547,0.00004325401,0.0004049843,0.0003102747,0.0000159289],"category_scores_gemma":[0.0001195438,0.0001809293,0.0001763719,0.0007293485,0.00006460262,0.0001217792,0.00006533119,0.0002191558,0.000001601126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630526,"about_ca_system_score_gemma":0.00002953622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005776036,"about_ca_topic_score_gemma":0.000001352572,"domain_scores_codex":[0.9986143,0.00001639376,0.000636513,0.0002178046,0.0002512785,0.0002637219],"domain_scores_gemma":[0.9990821,0.00009830693,0.000230217,0.0002412081,0.0002524577,0.00009567627],"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":[0.00009627545,0.00003040587,0.000005615669,0.0001836094,0.0001364425,0.00001181413,0.0001204109,0.02418822,0.9559385,0.0006292979,0.0004276968,0.0182317],"study_design_scores_gemma":[0.0002896706,0.0001560559,0.000005181439,0.0003617422,0.00007960075,0.0001138719,0.0006743534,0.01144644,0.9538916,0.0004510898,0.03234531,0.0001850407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8967348,0.004737933,0.09617944,0.0008079341,0.0008158273,0.00006782019,0.000008115461,0.0004671804,0.0001809033],"genre_scores_gemma":[0.992565,0.000336149,0.006774478,0.00002255415,0.0001640443,0.00001539436,0.000003616142,0.00006546997,0.00005325614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09583019,"threshold_uncertainty_score":0.7378084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006969021048681709,"score_gpt":0.2074807950370835,"score_spread":0.2005117739884018,"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."}}