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Record W2023488624 · doi:10.5539/jas.v2n1p144

Statistical Analysis of Main and Interaction Effects to Optimize Xylanase Production under Submerged Cultivation Conditions

2010· article· en· W2023488624 on OpenAlexvenueno aff
P. Mullai, N. Syed Ali Fathima, Eldon R. Rene

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
FundersIndian Institute of Technology Madras
KeywordsXylanaseInteractionResponse surface methodologySubstrate (aquarium)Production (economics)Analysis of varianceVariance (accounting)Main effectStatistical analysisEnvironmental scienceStatisticsMathematicsBiological systemAgricultural engineeringFood scienceBiotechnologyBiochemical engineeringChemistryBiologyEcologyEngineeringEnzymeBiochemistry

Abstract

fetched live from OpenAlex

In recent years, xylanase has become an essential option for environmental friendly industrial biotechnologicalapplications and there is a rising demand for large scale production. In this study, a Bacillus species 2129 was tested forthe xylanase production under submerged cultivation conditions. Maximum xylanase activities were achieved using oatas the substrate and by optimizing process conditions such as substrate concentration, pH and nitrogen source usingstatistically significant design of experiments, employing the response surface methodology (RSM) concept. Underoptimized conditions there was an 8% increase in the enzyme activity and results from statistical approximation in theform of analysis of variance (ANOVA) shows that the squared effects of the variables were significant than both themain and interaction effects.

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)0.021
metaresearch head score (Gemma)0.030
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 score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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