Approximation of the Effects of Subgrid Variations in Geometry in a Regional Ocean Model
Bibliographic record
Abstract
Accounting for the effects of subgrid features in a numerical model is a longstanding problem in surface water hydrodynamics. This problem arises in several ways: in many cases, objects in the flow are too small in scale to consider resolving, and in other cases, there can be tremendous savings in computer resources by limiting the resolution. At least two approaches can be used for estimating these effects. In a general method using double-averaging techniques, volume averages are calculated and include terms that arise from the stresses exerted by the subgrid objects. In another method primarily used to account for subgrid topographic variations, the subgrid data is incorporated directly into the model. The first method leads to the inclusion of form drag and is illustrated by studies of flow through vegetation on a river floodplain and tidal power potential of turbines placed in Minas Passage in the Bay of Fundy. The second method is illustrated by a study of tsunami runup on the west coast of Vancouver Island.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".