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Record W3115119340 · doi:10.1002/aic.17122

Modeling coupled temperature and transport effects on biofilm growth using thermal lattice Boltzmann model

2020· article· en· W3115119340 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAIChE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsAthabasca University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCellular automatonBiofilmLattice Boltzmann methodsThermalMaterials scienceChemistryThermodynamicsMechanicsBiological systemPhysicsMathematicsBiology

Abstract

fetched live from OpenAlex

Abstract This paper aims to develop an integrated thermal lattice Boltzmann model and cellular automata to investigate the effects of different temperatures and velocities on biofilm growth in a microbioreactor. Compared with previous studies this model accounted for direct effects of transient temperature on biofilm growth and indirect effects caused by changes of fluid properties. In addition, the algorithms have been improved on variations in solid boundary conditions, detachment and extra mass transport. Results showed that temperature affected both maximum biofilm concentration and growth rate. An increase of 10–75% in biofilm concentration was observed roughly due to increases in temperature. The time required to reach maximum concentration decreased from 30 days at a low temperature to 5 days at a high temperature. This demonstrates the capability of the present model to simulate biofilm behavior in the microbioreactor and its potential industrial and clinical applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.886

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 science0.0000.000
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 head0.024
GPT teacher head0.240
Teacher spread0.215 · 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