{"id":"W4295700902","doi":"10.15376/biores.17.4.6131-6147","title":"Impact of pre-extraction on xylose recovery from two lignocellulosic agro-wastes","year":2022,"lang":"en","type":"article","venue":"BioResources","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Xylose; Furfural; Hydrolysis; Chemistry; Xylitol; Bioconversion; Husk; Nuclear chemistry; Yield (engineering); Extraction (chemistry); Sulfuric acid; Chromatography; Food science; Fermentation; Biochemistry; Organic chemistry; Materials science; Catalysis; Botany; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009216756,0.0001280863,0.0001266121,0.0001339935,0.00008954947,0.00001793119,0.0001245648,0.0000441174,0.001100433],"category_scores_gemma":[0.000009822075,0.0001135651,0.0001383851,0.0001723649,0.00002701237,0.00007488297,0.00004282322,0.0001787253,0.00004217512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000115138,"about_ca_system_score_gemma":0.000009562698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003160789,"about_ca_topic_score_gemma":0.000001992215,"domain_scores_codex":[0.9992707,0.00004427759,0.0001655548,0.0001867291,0.0001968886,0.0001358786],"domain_scores_gemma":[0.9996185,0.00004636017,0.00006441151,0.0002085516,0.00001343661,0.00004868227],"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":[0.0006799911,0.000279005,0.01735764,0.00009610836,0.0002922082,0.000007557796,0.001105751,0.07087333,0.8568381,0.00001058196,0.02081603,0.03164368],"study_design_scores_gemma":[0.001902823,0.002296888,0.2028971,0.00007152378,0.0001171134,0.00001923436,0.001152706,0.01771761,0.6765484,0.0003849646,0.09598908,0.0009025241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972037,0.0007588604,0.00002242678,0.00005348744,0.000565551,0.0001182664,0.0001102257,0.0001671976,0.001000256],"genre_scores_gemma":[0.9992239,0.00007295579,0.00006675607,0.0000193999,0.0002748547,0.000007114267,0.00005406208,0.00002065157,0.0002602899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1855395,"threshold_uncertainty_score":0.9998127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094761058941563,"score_gpt":0.2336704566722878,"score_spread":0.2227228460828721,"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."}}