Comparison of numerical methods for modelling ocean circulation in basins with irregular coasts
Bibliographic record
Abstract
Comprehension of global oceanic currents and, ultimately, of climate variability requires the use of computer modelling. Although much effort has been spent on the accuracy of traditional finite difference (FD) models used in ocean modelling, there are still concerns, especially since these models have a crude representation of the geometry of oceanic basins. Such a crude representation may influence the accuracy of modelling boundary currents, or unrealisticly represent the impinging of eddies or the propagation of Kelvin waves along the coastline. This motivated the use of alternative modelling techniques applied on completely irregular geometries such as finite element (FE) and spectral element (SE) methods. In this thesis, we want to investigate the accuracy and cost-effectiveness of these three numerical methods in irregular domains and to understand to which extent the unstructured grid FE and SE methods constitute an improvement over the more traditional FD methods. To accomplish this, we limit ourselves to modelling the shallow water equations in presence of irregular coastlines with no bottom topography.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".