{"id":"W1993666667","doi":"10.1016/j.carbpol.2015.03.087","title":"Combining biomass wet disk milling and endoglucanase/β-glucosidase hydrolysis for the production of cellulose nanocrystals","year":2015,"lang":"en","type":"article","venue":"Carbohydrate Polymers","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Japan Science and Technology Agency; Financiadora de Estudos e Projetos; Ministry of Technology, Innovation and Citizens' Services; Japan International Cooperation Agency","keywords":"Cellulose; Cellulase; Hydrolysis; Crystallinity; Enzymatic hydrolysis; Thermostability; Bagasse; Materials science; Chemical engineering; Chemistry; Pyrococcus horikoshii; Lignin; Pulp (tooth); Pyrococcus furiosus; Nuclear chemistry; Organic chemistry; Composite material; Biochemistry; Pulp and paper industry; Enzyme","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.000891872,0.0002262537,0.0003560956,0.0001391486,0.00028388,0.00005538928,0.000246671,0.00005219711,0.00001339016],"category_scores_gemma":[0.0007176058,0.0001611383,0.00008861557,0.0003846977,0.0006114018,0.0002332411,0.0001745232,0.00008677941,0.000004381046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008139328,"about_ca_system_score_gemma":0.0001071002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005539619,"about_ca_topic_score_gemma":0.00004794879,"domain_scores_codex":[0.9981186,0.0001314764,0.0003723616,0.0004579588,0.0003992431,0.0005203357],"domain_scores_gemma":[0.9984649,0.000498464,0.0002357932,0.000407397,0.0001871879,0.0002061916],"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":[0.00009893938,0.00003159037,0.0001197071,0.00008161566,0.0000607636,0.000004377454,0.001217389,0.0002686836,0.9966942,0.0001871313,0.0001429676,0.001092649],"study_design_scores_gemma":[0.0004936614,0.0001433916,0.00002520965,0.00004730163,0.0001004883,0.000003661759,0.002178459,0.002279551,0.9935437,0.0003186137,0.000674512,0.0001913801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853492,0.01122924,0.0009541684,0.0008043422,0.0007324196,0.0006661447,0.00006173307,0.00005689557,0.0001458523],"genre_scores_gemma":[0.9983672,0.0002549773,0.0005517931,0.00002337,0.0001222877,0.0001664931,0.00001118105,0.00003295214,0.0004696871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01301805,"threshold_uncertainty_score":0.6571028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0333635469888844,"score_gpt":0.2772755492279058,"score_spread":0.2439120022390214,"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."}}