{"id":"W4403012801","doi":"10.1016/j.jobab.2024.09.004","title":"Enhanced biomass densification pretreatment using binary chemicals for efficient lignocellulosic valorization","year":2024,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Lignocellulosic biomass; Biomass (ecology); Pulp and paper industry; Environmental science; Biofuel; Bioenergy; Waste management; Biochemical engineering; Chemistry; Process engineering; Agronomy; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008397963,0.0003998453,0.000217616,0.0002144391,0.0001105484,0.0002147466,0.0001511703,0.0002019996,0.0006413825],"category_scores_gemma":[0.00007144067,0.0001329554,0.000260207,0.0001821586,0.0001399764,0.0002572849,0.0002432992,0.000364347,0.0001494332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002558376,"about_ca_system_score_gemma":0.0002207124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280305,"about_ca_topic_score_gemma":0.003237232,"domain_scores_codex":[0.9999253,0.000005751916,0.000006407285,0.00001629081,0.00002647704,0.0000198239],"domain_scores_gemma":[0.9999746,0.000003483204,0.000006987324,0.000002866818,0.0000075905,0.000004433825],"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":[0.000008608038,0.000007817792,0.00008346831,0.00002766147,0.000002576122,0.00001377123,0.00000606745,0.0001145062,0.9984097,0.00006913453,0.000009622468,0.001246976],"study_design_scores_gemma":[0.000002640193,0.0000355696,0.0007345232,0.000001765614,0.00000564732,0.00002252013,0.000009199513,0.001003089,0.9974524,0.00001697168,0.0007131401,0.000002633893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905863,0.0009119561,0.007086273,0.00005279822,0.00002051719,0.00002996355,0.000087111,0.00007094336,0.001154117],"genre_scores_gemma":[0.9946778,0.0004594117,0.003667911,0.0000217704,0.000005050382,0.00001319718,0.000103469,0.000008633173,0.001042774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001280305,"threshold_uncertainty_score":0.002545714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597701301336971,"score_gpt":0.2307609643802412,"score_spread":0.2147839513668715,"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."}}