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Record W2316796749 · doi:10.1142/9789812702234_0216

SIMULATED EPICARDIAL POTENTIAL MAPS WITH A MEMBRANE-BASED BIDOMAIN MODEL OF THE HUMAN HEART

2005· article· en· W2316796749 on OpenAlexaffabout
Mark Potse, Bruno‐Pierre Dubé, ERIC BÉLANCER, J. Richer, R.M. Gulrajani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBidomain modelHuman heartComputer scienceCardiologyInternal medicinePhysicsMedicine

Abstract

fetched live from OpenAlex

québécois de calcul de haute performance, Montréal (Québec), Canada. Computer models of cardiac activation are constantly growing in terms of accuracy and resource usage. The availability of powerful parallel computers has allowed us to create a program that computes intracellular and extracellular potentials in a model of a complete human heart, basing on a model of the ionic currents in the cell membrane and the bidomain model of cardiac tissue. Potentials were computed on a regular finite-difference grid of 50 million nodes, using a forward-Euler approximation for the membrane potential and a BiCGStab linear-system solver with a parallelized incomplete-LU preconditioner to solve for the extracellular potentials. Realistic extracellular signals and epicardial potential distributions were obtained. 1

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.187
Teacher spread0.179 · 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 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

Citations1
Published2005
Admission routes2
Has abstractyes

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