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Record W2056876618 · doi:10.1002/cjce.22172

A hybrid model for biofilm growth on a deformable substratum

2015· article· en· W2056876618 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsBiofilmCellular automatonFinite element methodAttractorCurvatureBiological systemProcess (computing)Growth modelMechanicsComputer scienceMathematicsGeologyPhysicsEngineeringMathematical analysisStructural engineeringGeometryBiologyArtificial intelligenceBacteria

Abstract

fetched live from OpenAlex

The mutual interaction between a biofilm growing on a deformable substratum and its deformability is investigated. The interaction process is investigated by a newly developed model based on a hybrid Cellular Automaton/Finite Element approach (CAFE). A quantitative model is proposed that predicts the effect of the substratum deformability on the biofilm growth as well as on the allocation of the newborn cells. In the proposed model, it is suggested that regions of higher positive curvature will act as attractors. The finite element method is used to model the substratum deformability while the biofilm growth is modelled using a semi‐stochastic approach. Numerical examples are presented in two‐ and three‐dimensional settings.

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 score0.235
Threshold uncertainty score0.372

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 integrity0.0000.000
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 head0.024
GPT teacher head0.196
Teacher spread0.171 · 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