{"id":"W2755807287","doi":"10.1016/j.biortech.2017.09.080","title":"Ethanol production from bamboo using mild alkaline pre-extraction followed by alkaline hydrogen peroxide pretreatment","year":2017,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Tianjin University of Science and Technology","keywords":"Chemistry; Hydrogen peroxide; Sodium hydroxide; Extraction (chemistry); Cellulose; Hydrolysate; Xylose; Fermentation; Nuclear chemistry; Chromatography; Ethanol fuel; Enzymatic hydrolysis; Hydrolysis; Alkaline hydrolysis; Biochemistry; Organic chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001307669,0.000314845,0.0002821738,0.0002603888,0.0004010718,0.00005612804,0.0003885734,0.0006083399,0.0001302086],"category_scores_gemma":[0.00007805193,0.0003064593,0.00009686904,0.0001660219,0.0002272473,0.0002153199,0.0001193846,0.0003465931,0.000120615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242306,"about_ca_system_score_gemma":0.00001377034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003807034,"about_ca_topic_score_gemma":0.0000240383,"domain_scores_codex":[0.9985073,0.00002648554,0.0003098014,0.0005920831,0.0001943699,0.0003699445],"domain_scores_gemma":[0.998572,0.000008819173,0.0001824015,0.001100791,0.00005257171,0.000083447],"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":[0.0000488309,0.00009084887,0.002050129,0.00003169734,0.0001113527,0.000005595951,0.0000692373,0.0002355141,0.9874144,0.000004287951,0.005589762,0.004348309],"study_design_scores_gemma":[0.000584436,0.00007985384,0.000489799,0.00004058778,0.00008999209,0.00002986173,0.0001003088,0.004783893,0.8688227,0.00008313153,0.1245871,0.0003083881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992813,0.001125672,0.0008110437,0.002188133,0.0009484771,0.0005241737,0.00005281434,0.001365398,0.0001712724],"genre_scores_gemma":[0.9959927,0.000254056,0.002264766,0.0000331249,0.0003871572,0.00003566546,0.0001032034,0.00006277313,0.0008665795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1189973,"threshold_uncertainty_score":0.9999387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705496551469915,"score_gpt":0.255621089296121,"score_spread":0.2385661237814218,"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."}}