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Statistical optimization of culture conditions for enhanced xylanase production by bacillus species using response surface methodology

2025· article· W7128295921 on OpenAlexaboutno aff
Michael Thompson, Jennifer Williams, David Anderson, Emily Martin

Bibliographic record

VenueInternational Journal of Advanced Biochemistry Research · 2025
Typearticle
Language
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsResponse surface methodologyXylanaseCentral composite designBacillus (shape)BacillaceaeStatistical analysisEnzymeBacillales

Abstract

fetched live from OpenAlex

Global enzyme markets valued at roughly 12.3 billion USD in 2023 continue to expand, and xylanases account for a growing share of industrial demand. This research optimized culture conditions for xylanase production by a locally isolated Bacillus species (strain BX-17) using Response Surface Methodology (RSM) based on a Central Composite Design (CCD). The organism was isolated from decomposing hardwood samples collected near Toronto, Canada, and identified through 16S rRNA gene sequencing. Initial screening of six variables through a Plackett-Burman design identified temperature, pH, and substrate concentration as the three most significant factors affecting enzyme yield. A 2³ CCD with five center points generated 20 experimental runs. The resulting quadratic model showed strong fit (R² = 0.964, adjusted R² = 0.938, p

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.422
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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