MétaCan
Menu
Back to cohort
Record W2081912752 · doi:10.1137/100819229

Continuity of Resetting a Pacemaker in an Excitable Medium

2011· article· en· W2081912752 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.

Bibliographic record

VenueSIAM Journal on Applied Dynamical Systems · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsMcGill University
Fundersnot available
KeywordsStimulus (psychology)Excitable mediumAmplitudeContinuationControl theory (sociology)Phase response curveMechanicsMathematicsPhysicsComputer scienceNeuroscienceArtificial intelligencePsychologyOptics

Abstract

fetched live from OpenAlex

Pacemakers in excitable media generate waves that propagate outward from the pacemaker. Such waves of excitation are well known in biological and chemical systems such as nerves, the heart, and the Belousov–Zhabotinsky reaction. Stimuli delivered at a distant site from the pacemaker can reset the pacemaker, leading to a change in the timing of the pacemaker. The relation between stimulus timing and resultant resetting of the pacemaker is captured by phase resetting curves. The continuity of resetting curves has been investigated in both experiments and numerical models. We present theoretical results discussing conditions for continuity of resetting curves as the amplitude and phase of the stimulus varies. We also use continuation and shooting methods to analyze the continuity of resetting curves in simple mathematical models of cardiac and neural activity. Under continuous changes of stimulus parameters, resetting curves will be continuous unless a stimulus leads to dynamics that fall outside the basin of attraction of the pacemaker-driven excitable medium.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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