Bibliographic record
Abstract
Xylanase breaks xylan down to xylose which is used in industries such as pulp and paper, food, and feed, among others. The utilization of wastes for xylanase production is economical, hence, this work aimed at producing xylanase through solid-state fermentation and characterizing the enzyme. Xylanase-producing strains of Bacillus megaterium and Aspergillus niger GIO were inoculated separately in a 5 and 10 days solid fermentation study on maize straw, rice straw, sawdust, corn cob, sugarcane bagasse, conifer liters, alkaline-pretreated maize straw (APM), and combined-alkaline and biological-pretreated maize straw, respectively. The best substrate was selected for xylanase production. The crude enzyme was extracted from the fermentation medium and xylanase activity was characterized using parameters such as temperature, cations, pH, and surfactants. Among different substrates, the highest xylanase activity of 3.18 U/mL was recorded when Aspergillus niger GIO was grown on APM. The xylanase produced by Aspergillus niger GIO and Bacillus megaterium had their highest activities (3.67 U/mL, 3.36 U/mL) at 40 °C after 30 and 45 minutes of incubation, respectively. Optimum xylanase activities (4.58 and 3.58 U/mL) of Aspergillus niger GIO and Bacillus megaterium, respectively were observed at pH 5.0 and 6.2. All cations used enhanced xylanase activities except magnesium ion. Sodium dodecyl sulfate supported the highest xylanase activity of 6.13 and 6.90 U/mL for Aspergillus niger GIO and Bacillus megaterium, respectively. High yields of xylanase were obtained from Aspergillus niger GIO and Bacillus megaterium cultivated on alkaline pretreated maize straw. The xylanase activities were affected by pH, temperature, surfactants, and cations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.783 | 0.522 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".