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Record W1999766305 · doi:10.1080/00986445.2013.819351

APPLICATION OF POWER LAW LOGISTIC MODEL TO GROWTH KINETICS OF<i>BACILLUS LICHENIFORMIS</i>MS3 ON A WATER-INSOLUBLE SUBSTRATE

2013· article· en· W1999766305 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Engineering Communications · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBacillus licheniformisChemistryDecaneBiodegradationHydrocarbonSubstrate (aquarium)PopulationChromatographyMass transferKineticsBacterial growthChemical engineeringBacteriaOrganic chemistryEcologyBiology

Abstract

fetched live from OpenAlex

The power law logistic model was utilized to investigate the growth of a hydrocarbon assimilating bacterium on a water-insoluble substrate. To achieve this end, population dynamics of Bacillus licheniformis MS3 in a medium containing n-decane as the sole carbon source was monitored for 30 h. Different initial biosurfactant concentrations and shaking rates were employed to examine the role of mass transfer in the cell growth and the consequent hydrocarbon biodegradation. The amount of n-decane degraded in the system was detected by gas chromatography at the end of the incubation period. The results revealed that when mass transfer limitations were lessened through addition of an initial biosurfactant concentration and agitation, the bacterial growth increased more than three times and the n-decane biodegradation was enhanced from 6.7 to 15.1 mg/100 mL. Finally, the power law logistic model proved to be highly capable in simulating both the experimental results and various systems with water-insoluble carbon sources.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.041
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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