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

2025· article· W7128295921 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.324
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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