{"id":"W4392858172","doi":"10.1016/j.jobab.2024.03.002","title":"Production of chitosan-based composite film reinforced with lignin-rich lignocellulose nanofibers from rice husk","year":2024,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Ministry of SMEs and Startups; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Lignin; Husk; Cellulose; Enzymatic hydrolysis; Chitosan; Biomass (ecology); Nanofiber; Hydrolysis; Chemistry; Materials science; Chemical engineering; Polymer; Pulp and paper industry; Organic chemistry; Composite material; Botany; Agronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004561359,0.0002353725,0.0004401808,0.0002826728,0.0001603306,0.0001270323,0.0002394985,0.00006431518,0.00001762911],"category_scores_gemma":[0.00009508451,0.0001453478,0.00007655189,0.0005825197,0.0004434818,0.0003055001,0.00006429893,0.00021616,0.000005631373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002596521,"about_ca_system_score_gemma":0.0001105232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004677738,"about_ca_topic_score_gemma":0.000001915115,"domain_scores_codex":[0.9979352,0.00007788013,0.0005508002,0.0004181651,0.000705055,0.0003129341],"domain_scores_gemma":[0.9988039,0.0001463054,0.0004260926,0.0002672331,0.0002151774,0.0001413448],"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":[0.0003970953,0.00004802914,0.0003131204,0.0003505105,0.00009059103,0.00007712079,0.001141115,0.000768126,0.9956114,0.00001019378,0.0004098738,0.000782821],"study_design_scores_gemma":[0.0003809544,0.0007272696,0.00105927,0.0006672212,0.0001033854,0.0000639865,0.0004954861,0.0002200476,0.9949368,0.00003776702,0.001119576,0.0001882592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877717,0.009726857,0.0001043758,0.00148433,0.000508678,0.0002062696,0.00002686046,0.00003771428,0.0001332208],"genre_scores_gemma":[0.9918411,0.000671924,0.006456965,0.00002643393,0.0007135553,0.000002825118,0.000005477827,0.00002375223,0.0002579909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009054933,"threshold_uncertainty_score":0.5927113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077288791306885,"score_gpt":0.2383475356349126,"score_spread":0.2275746477218438,"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."}}