MétaCan
Menu
Back to cohort
Record W1983232546 · doi:10.1155/2014/498184

Correlation and Spectral Properties of a Coupled Nonlinear Dynamical System in the Context of Numerical Weather Prediction and Climate Modeling

2014· article· en· W1983232546 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

VenueDiscrete Dynamics in Nature and Society · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAutocorrelationNonlinear systemStatistical physicsSpectral densityData assimilationDynamical systems theoryNumerical weather predictionComputer scienceSpectral spaceNonlinear dynamical systemsCoupling (piping)Temporal scalesContext (archaeology)Dynamical system (definition)MeteorologyPhysicsMathematicsGeologyStatistics

Abstract

fetched live from OpenAlex

Complex dynamical processes occurring in the earth’s climate system are strongly nonlinear and exhibit wave-like oscillations within broad time-space spectrum. One way to imitate essential features of such processes is using a coupled nonlinear dynamical system, obtained by coupling two versions of the well-known Lorenz (1963) model with distinct time scales that differ by a certain time-scale factor. This dynamical system is frequently applied for studying various aspects of atmospheric and climate dynamics, as well as for estimating the effectiveness of numerical algorithms and techniques used in numerical weather prediction, data assimilation, and climate simulation. This paper examines basic dynamic, correlative, and spectral properties of this system and quantifies the influence of the coupling strength on power spectrum densities, spectrograms, and autocorrelation functions.

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)0.000
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 score0.815
Threshold uncertainty score0.175

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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