{"id":"W4417072514","doi":"10.1016/j.biombioe.2025.108788","title":"β-Glucanase engineering oriented to industrial biorefinery: Database-based directed evolution for thermostability optimization","year":2025,"lang":"en","type":"article","venue":"Biomass and Bioenergy","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Science and Technology Bureau of Zhenjiang; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Thermostability; Directed evolution; Biorefinery; Catalysis; Xylanase; DNA shuffling; Protein engineering; Catalytic efficiency; Biomass (ecology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001276268,0.0001509323,0.0001282977,0.0002349709,0.00007586226,0.00002921776,0.00006039236,0.0001345391,0.00001616763],"category_scores_gemma":[0.00006483213,0.0001355375,0.00003819974,0.0005866735,0.00002357773,0.00006665961,0.00002722433,0.00004789793,7.147142e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185314,"about_ca_system_score_gemma":0.00003912768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001499369,"about_ca_topic_score_gemma":0.00002023521,"domain_scores_codex":[0.9992773,0.00001560054,0.000176929,0.0002671834,0.00007253767,0.0001904763],"domain_scores_gemma":[0.9996257,0.00002573911,0.00001926091,0.0001797241,0.00006361787,0.00008592485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005508688,0.0001635556,0.002096411,0.0007374414,0.0001341923,0.000001869926,0.00002661674,0.07023554,0.8977982,0.001686167,0.0143853,0.01218378],"study_design_scores_gemma":[0.00139133,0.00009253108,0.0009628424,0.00007282013,0.00003830193,6.770217e-7,0.00002990755,0.6746936,0.2129984,0.000006330027,0.1094063,0.0003070026],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4367495,0.0006511627,0.5550064,0.00177784,0.003113434,0.0008946239,0.0004308254,0.001244161,0.0001321011],"genre_scores_gemma":[0.9920048,0.00001757074,0.007209121,0.0000770159,0.0001638829,0.0000615461,0.0003860297,0.00001791843,0.00006217071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6847999,"threshold_uncertainty_score":0.5527058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293430215814551,"score_gpt":0.2079173995853845,"score_spread":0.194983097427239,"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."}}