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Record W2068154710 · doi:10.1142/s0219477508005033

NON-GAUSSIAN NOISE-ENHANCED INTRINSIC STOCHASTIC OSCILLATIONS IN CATALYTIC NO REDUCTION WITH CO ON SMALL-SIZE PT SURFACES

2008· article· en· W2068154710 on OpenAlexfundno aff
Yubing Gong, Yanhang Xie, Yinghang Hao, Bo Xu

Bibliographic record

VenueFluctuation and Noise Letters · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsnot available
FundersMcGill University
KeywordsGaussian noiseNoise (video)GaussianStatistical physicsPhysicsMathematicsComputer scienceQuantum mechanicsAlgorithm

Abstract

fetched live from OpenAlex

We numerically study the effect of a particular kind of non-Gaussian colored noise (NGN) on the intrinsic stochastic reaction rate oscillations (RRO) in NO reduction with CO on small-size Pt surfaces. It was found that the RRO can be enhanced by the appropriate noise strength, correlation time, in particular, the deviation of the NGN from Gaussian noise. Furthermore, there is an optimal NGN by which the RRO can be most greatly enhanced. This result shows that the RRO can be enhanced by different types of the NGN, and can reach the best oscillatory state by utilizing one optimal type of the noise. A simple explanation for the phenomenon is given.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.716

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 designObservational
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
Published2008
Admission routes1
Has abstractyes

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