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Record W1965170232 · doi:10.1029/2004gl020840

Effects of time averaging on climate regimes

2004· article· en· W1965170232 on OpenAlexafffund
Qiaobin Teng, Adam H. Monahan, John C. Fyfe

Bibliographic record

VenueGeophysical Research Letters · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Victoria
FundersCanadian Institute for Advanced Research
KeywordsExtratropical cycloneClimatologyClimate modelEnvironmental scienceClimate changeNorthern HemisphereAtmospheric sciencesAtmosphere (unit)GaussianResidence time (fluid dynamics)Statistical physicsMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

The effects of different temporal averaging timescales on the regime structure of the extratropical Northern Hemisphere atmosphere in reanalysis data and in a global climate model are considered. It is shown that temporal averaging can significantly change the regime structure when the averaging timescale is considerably longer than the intrinsic regime residence times. These effects are explained using a simple stochastic model of motion in a 3‐well potential. This study highlights the importance of the careful choice of averaging timescales in the analysis of non‐Gaussian structure in climate data.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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 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

Citations16
Published2004
Admission routes2
Has abstractyes

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