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Record W2095345721 · doi:10.1103/physreve.65.021908

Predicting the entrainment of reentrant cardiac waves using phase resetting curves

2002· article· en· W2095345721 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

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2002
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsMontreal Heart InstituteMcGill University
FundersMedical Research CouncilHeart and Stroke Foundation of Canada
KeywordsReentrancyEntrainment (biomusicology)ExcitationStimulus (psychology)PhysicsRhythmReentryTime domainMechanicsComputer scienceCardiologyAcousticsMedicineCondensed matter physicsQuantum mechanicsPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Excitable media, such as the Belousov-Zhabotinsky medium or the heart, are capable of supporting excitation waves that circulate in a closed repetitive path--a phenomenon known as reentrant excitation. A single stimulus, depending on its magnitude, timing, and location, can cause a time shift of the reentrant excitation called resetting. The present study examines the ability of resetting data to predict the effects of periodic stimuli on reentrant excitation circulating on an annular domain. We compare the results of the theoretical models with experiments carried out in an animal model of a dangerous reentrant cardiac rhythm. The current work may lead to improved approaches to therapy through a better understanding of how typical clinical stimuli interact with abnormal reentrant cardiac rhythms.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.022
GPT teacher head0.323
Teacher spread0.301 · 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