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Record W2081876020 · doi:10.1139/w00-064

Stoichiometry of diauxic growth of a xylanase-producing<i>Bacillus</i>strain

2000· article· en· W2081876020 on OpenAlexvenueno aff
Gerhard Schneider, Patricia Taillandier, Pierre Strehaiano

Bibliographic record

VenueCanadian Journal of Microbiology · 2000
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsFermentationXylanaseBiomass (ecology)Carbon dioxideChemistryFood scienceXylanAerationBacteriaCarbon fibersBacterial growthStrain (injury)Pulp and paper industryBiochemistryBiologyPolysaccharideOrganic chemistryEnzymeMaterials scienceAgronomy

Abstract

fetched live from OpenAlex

In this work, the establishment of material balances and stoichiometry of the growth of Bacillus sp. was undertaken. This strain produces high quantities of a xylanase suitable for use as bleach boost agent in chlorine-free bleaching sequences of paper pulp. As carbon dioxide plays an important role as a growth factor, bacterial growth in two fermentations, one fed with air and another fed with carbon-dioxide-enriched air, were compared. For this purpose, a method permitting the determination of the consumption of the two carbon sources, xylan and peptone, was proposed. The material balances revealed that in both cases, the bacteria first use peptone as their carbon source, and then xylan in the second part of the growth phase. The aerated culture showed diauxic growth on these two substrates, whereas carbon-dioxide-enriched air caused disappearance of the metabolic adaptation phase, and rendered biomass production more economic. The fermentation fed with air needed 30% more xylan than the fermentation fed with carbon-dioxide-enriched air for the same quantity of biomass produced.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.176
Teacher spread0.169 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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