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Record W2056736765 · doi:10.1142/s0218127410026824

NOISE-INDUCED SYNCHRONIZATION IN MULTITROPHIC CHAOTIC ECOLOGICAL SYSTEMS

2010· article· en· W2056736765 on OpenAlexaff
Daihai He, Lewi Stone, Bernard Cazelles

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsMcMaster University
FundersJames S. McDonnell Foundation
KeywordsSynchronization (alternating current)Forcing (mathematics)Noise (video)ChaoticStatistical physicsVariable (mathematics)EcologyControl theory (sociology)Synchronization of chaosComputer scienceFocus (optics)Chaotic systemsPhysicsMathematicsBiologyTelecommunicationsMathematical analysisArtificial intelligenceAtmospheric sciences

Abstract

fetched live from OpenAlex

Noise-induced synchronization is an unexpected phenomenon which we show here to have biological relevance for multitrophic ecological systems. We focus on two uncoupled Hastings–Powell systems having oscillatory dynamics. Despite the fact the systems are uncoupled, it is shown that they can nevertheless synchronize by applying common Gaussian noise forcing to a suitably chosen variable in both systems. Complete synchronization is studied in the case of identical oscillators, and intermittent synchronization is analyzed when the oscillators are nonidentical. We discuss how this unusual synchronization phenomenon has features akin to Ecology's well-known "Moran effect."

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.202

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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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