Modeling events of sea‐surface variability using spectral nudging in an eddy permitting model of the northeast Pacific Ocean
Bibliographic record
Abstract
Eddies are an important part of the current system that hugs the coasts of British Columbia and Alaska. The ability of “spectral nudging” to improve the eddy statistics determined from model simulations of this current system is investigated. Spectral nudging differs from standard nudging in that only specified frequency and wave number bands of the simulated potential temperature and salinity fields are nudged toward the observed climatology. Therefore the simulated eddy field can develop and evolve with time while the model is prevented from drifting far from the observed climatology. The Parallel Ocean Program (POP) is used to do the simulations, with 0.25° horizontal resolution and 23 vertical levels. The simulated standard deviation and skewness fields for the sea surface height are compared with those estimated from ten years of TOPEX/Poseidon altimetry observations. This comparison shows that spectral nudging allows the model to simulate the eddy statistics of the current system with significantly more accuracy than when the nudging is not used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".