Downscaling ocean conditions with application to the Gulf of Maine, Scotian Shelf and adjacent deep ocean
Bibliographic record
Abstract
Abstract The overall goal is to downscale ocean conditions predicted by an existing global prediction system and evaluate the results using observations from the Gulf of Maine, Scotian Shelf and adjacent deep ocean. The first step is to develop a one-way nested regional model and evaluate its predictions using observations from multiple sources including satellite-borne sensors of surface temperature and sea level, CTDs, Argo floats and moored current meters. It is shown that the regional model predicts more realistic fields than the global system on the shelf because it has higher resolution and includes tides that are absent from the global system. However, in deep water the regional model misplaces deep ocean eddies and meanders associated with the Gulf Stream. This is not because the regional model’s dynamics are flawed but rather is the result of internally generated variability in deep water that leads to decoupling of the regional model from the global system. To overcome this problem, the next step is to spectrally nudge the regional model to the large scales (length scales > 90 km) of the global system. It is shown this leads to more realistic predictions off the shelf. Wavenumber spectra show that even though spectral nudging constrains the large scales, it does not suppress the variability on small scales; on the contrary, it favours the formation of eddies with length scales below the cutoff wavelength of the spectral nudging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".