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Record W1990484473 · doi:10.1103/physreve.67.031914

Robustness and perturbation in the modeled cascade heart rate variability

2003· article· en· W1990484473 on OpenAlexaff
D. C. Lin

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMultifractal systemCascadeMultiplicative functionScalingStatistical physicsPerturbation (astronomy)Robustness (evolution)Heart rate variabilityMathematicsPhysicsFractalMathematical analysisHeart rateQuantum mechanicsGeometryChemistryBlood pressureMedicineInternal medicine

Abstract

fetched live from OpenAlex

In this study, numerical experiments are conducted to examine the robustness of using cascade to describe the multifractal heart rate variability (HRV) by perturbing the hierarchical time scale structure and the multiplicative rule of the cascade. It is shown that a rigid structure of the multiple time scales is not essential for the multifractal scaling in healthy HRV. So long as there exists a tree structure for the multiplication to take place, a multifractal HRV and related properties can be captured by using the cascade. But the perturbation of the multiplicative rule can lead to a qualitative change. In particular, a multifractal to monofractal HRV transition can result after the product law is perturbed to an additive one at the fast time scale. We suggest that this explains the similar HRV scaling transition in the parasympathetic nervous system blockade.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

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