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Record W2789392647 · doi:10.1002/cjce.23171

A sensitivity analysis for tissue development by varying model parameters and input variables

2018· article· en· W2789392647 on OpenAlexvenueno aff
Ágata Paim, Isabel Cristina Tessaro, Patrícia Pranke, Nilo Sérgio Medeiros Cardozo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsnot available
Fundersnot available
KeywordsDimensionless quantitySensitivity (control systems)Biological systemVolume fractionDiffusionGrowth modelPorosityMechanicsPhenomenological modelCell growthMaterials scienceChemistryMathematicsBiomedical engineeringThermodynamicsPhysicsStatisticsEngineeringBiologyBiochemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Phenomenological models can help in the study of the relation between mass transport and cell growth in three‐dimensional porous scaffolds, which is one of the main challenges of tissue engineering. Thus, using a model for cell proliferation and glucose diffusion and consumption, the dimensionless parameters and input variables were varied to determinate those which affect the model output the most. The simulations were performed with the software OpenFOAM, and the results were compared through a sensitivity analysis for the parameters. It was observed that the model is more sensitive to the dimensionless parameters related to cell proliferation, death, and nutrient uptake, and that dimensionless initial glucose concentration and scaffold porosity had a higher impact on cell volume fraction and on dimensionless glucose concentration. When compared to data from experimental studies, the computational results showed that the studied model is capable of representing the phenomena involved in tissue development in vitro.

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 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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.719
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.047
GPT teacher head0.265
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

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