{"id":"W588713518","doi":"10.1016/j.biortech.2015.06.011","title":"Mechanical pretreatment improving hemicelluloses removal from cellulosic fibers during cold caustic extraction","year":2015,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Tianjin Science and Technology Committee; Beijing Municipal Science and Technology Commission; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Cellulosic ethanol; Caustic (mathematics); Extraction (chemistry); Cellulose; Pulp and paper industry; Hemicellulose; Waste management; Polymer science; Chemistry; Materials science; Chromatography; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001147239,0.0002940524,0.0003567181,0.0001568965,0.0002490646,0.0002639503,0.0001949685,0.0002285012,0.001027657],"category_scores_gemma":[0.0001479125,0.0001592599,0.0003299271,0.0002058827,0.0001677837,0.0003421577,0.0001627262,0.0003855325,0.0001999723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002233697,"about_ca_system_score_gemma":0.0002265054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001296574,"about_ca_topic_score_gemma":0.003148341,"domain_scores_codex":[0.9999095,0.000007209645,0.000008279043,0.00001900141,0.00002679465,0.00002919319],"domain_scores_gemma":[0.9999245,0.00001505574,0.00002339092,0.000008010193,0.0000196765,0.000009302644],"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":[0.00005553248,0.00001483916,0.0001103767,0.00003172031,0.000004199292,0.00002689871,0.00001531523,0.00008360257,0.9985225,0.00001758856,0.00001246704,0.0011049],"study_design_scores_gemma":[0.000003346871,0.00008121264,0.003012466,0.000002903744,0.00001365889,0.00003339698,0.00002023218,0.0004429609,0.9960598,0.000009631733,0.0003168285,0.000003677049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977804,0.0004692967,0.001228018,0.00002355389,0.00001349128,0.000009098709,0.00002814336,0.00001252482,0.0004355457],"genre_scores_gemma":[0.9976349,0.0003243781,0.00092149,0.00001777412,0.000006558174,0.000007634492,0.00004999077,0.000009366134,0.001027878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001296574,"threshold_uncertainty_score":0.003437817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224775578621097,"score_gpt":0.2718881353668404,"score_spread":0.2496403795806295,"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."}}