Internal tide generation from non-uniform barotropic body forcing
Bibliographic record
Abstract
We model linear, inviscid non-hydrostatic internal tides generated by the interaction of a barotropic tide with variable topography in two dimensions. We first derive an asymptotic solution for the nonuniform barotropic flow over the topography that serves as forcing for the baroclinic equations. The resulting internal-tide generation problem is reformulated as a Coupled-Mode System (CMS) by means of a series decomposition of the baroclinic stream function in terms of vertical basis functions. We solve this CMS numerically and also provide a method for estimating the sea-surface signature of internal tides. We consider several seamounts and shelf profiles and perform calculations for a wide range of (topographic) heights and slopes. For subcritical topographies, the energy flux as a function of height exhibits local maxima, separated by cases of weakly- or even non-radiating topographies. For supercritical topographies, the energy flux generally increases with height and criticality. Our calculations agree with the Weak Topography Approximation only for very small heights. Perhaps more surprisingly, they agree with the Knife Edge model only for moderately supercritical topographies. We also compare the effect of the adjusted barotropic tide on the energy flux and the local properties of the baroclinic field with other semi-analytical methods based on a uniform barotropic tide. We observe significant differences in the flow field near the topographies only.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".