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Euler–Poincaré theorem for geophysical fluid dynamics

2009· book-chapter· en· W4388322952 on OpenAlexaff
Darryl D. Holm, Tanya Schmah, Cristina Stoica, David Ellis

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsWilfrid Laurier UniversityUniversity of Toronto
Fundersnot available
KeywordsGeophysical fluid dynamicsEuler's formulaNonlinear systemAtmosphere (unit)Scale (ratio)General Circulation ModelPartial differential equationOcean dynamicsCirculation (fluid dynamics)GeophysicsOcean currentClimate changeClimatologyApplied mathematicsMathematicsGeologyMeteorologyGeographyMechanicsMathematical analysisPhysicsOceanographyCartography

Abstract

fetched live from OpenAlex

Abstract Geophysical fluid dynamics (GFD) studies the large-scale circulatory motions of the atmosphere and ocean, which are of importance, for example, in modelling climate change. The history of predicting climate, or rather predicting the general circulation in the atmosphere and ocean, records a series of challenges that were met and overcome in formulating the important questions and finding numerical solutions to the nonlinear partial differential equations (PDE) that govern climate dynamics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.022
GPT teacher head0.217
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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