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Record W1968273758 · doi:10.1098/rspa.2001.0845

A period–doubling bifurcation with slow parametric variation and additive noise

2001· article· en· W1968273758 on OpenAlexaff
Huw G. Davies, Krishna Rangavajhula

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBifurcationPeriod-doubling bifurcationPeriod (music)MathematicsNoise (video)Orbit (dynamics)AmplitudeModulation (music)Parametric statisticsControl theory (sociology)PhysicsStatisticsNonlinear systemOpticsComputer scienceAcousticsQuantum mechanicsControl (management)

Abstract

fetched live from OpenAlex

Slow sinusoidal modulation of a control parameter can maintain a low–period orbit into parameter regions where the low–period orbit is locally unstable, and a higherperiod orbit would normally occur. Whether or not a bifurcation to higher period becomes evident during the modulation depends on the competing effects of stabilization by the modulation and destabilization by inherent very low level system noise. A transition, often rapid, from a locally unstable period–1 orbit to period–2, for example, can be triggered by noise. The competing effects are examined here for a period–doubling bifurcation of a general unimodal map. A nested set of three matched asymptotic expansions (a triple–deck) is used to describe the combined period–1 and period–2 response. The resulting solution gives estimates of whether and where an apparent period–doubling bifurcation occurs. Typical period–1 stability boundaries are obtained that include the effect of the amplitude and frequency of the variation, the noise level in the system, and the allowable maximum threshold level of period–2 response.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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 designTheoretical or conceptual
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

Citations12
Published2001
Admission routes1
Has abstractyes

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