{"id":"W4388591677","doi":"10.1016/j.biortech.2023.130000","title":"Lignocellulosic biomass pretreatment: Industrial oriented high-solid twin-screw extrusion method to improve biogas production from forestry biomass resources","year":2023,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; University of Ottawa; Lakehead University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centre of Innovation","keywords":"Biomass (ecology); Biogas; Lignocellulosic biomass; Environmental science; Bioenergy; Pulp and paper industry; Waste management; Biofuel; Agricultural engineering; Engineering; Agronomy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003506357,0.0005112744,0.0004717784,0.001756155,0.0002537779,0.00005766467,0.0004957275,0.00117822,0.00009991277],"category_scores_gemma":[0.0001523664,0.0004695036,0.0001310498,0.003756283,0.0001913779,0.000121713,0.0003928072,0.000462729,0.0006887896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002555232,"about_ca_system_score_gemma":0.00002670826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002951327,"about_ca_topic_score_gemma":0.00001447992,"domain_scores_codex":[0.9971176,0.00009273584,0.0005404214,0.001124819,0.0003600577,0.0007644087],"domain_scores_gemma":[0.9985223,0.00005103416,0.000141425,0.0009789197,0.00009057254,0.0002156853],"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":[0.0001569109,0.00008904344,0.004241294,0.00006436927,0.0001742163,0.0000342919,0.0002223701,0.0000827413,0.9517744,0.0001011895,0.01350202,0.02955718],"study_design_scores_gemma":[0.0008934669,0.0003326272,0.001148369,0.00006463146,0.0000760074,0.00001348544,0.0004698465,0.0006454409,0.7826391,0.0004861411,0.2127794,0.0004514797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829569,0.0003418875,0.00156839,0.005500142,0.002645936,0.001082221,0.0001660477,0.005640502,0.00009795391],"genre_scores_gemma":[0.9923011,0.0000635075,0.005103834,0.00004501353,0.001206216,0.0001669752,0.0002664335,0.0001250589,0.0007218615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1992774,"threshold_uncertainty_score":0.9997756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574012535133996,"score_gpt":0.2387781664507531,"score_spread":0.2230380410994131,"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."}}