The influence of a geostrophic current on the internal tide generation
Bibliographic record
Abstract
We investigate the influence of a barotropic geostrophic current on internal tide (IT) generation over a shelf slope. The current $V_g(x)$ is modeled as an idealized Gaussian function centered at $x_0$ with width $x_r$ and maximum velocity $V_{max}$. The bathymetry is modelled as a linear slope with smoothed corners. We calculate the total barotropic-to-baroclinic energy conversion $C = \int \overbar{C} \,dx = \int \int \rho' g W \,dx\, dz$. $\overbar{C}(x,t)$ can be either positive or negative. Positive (negative) conversion means energy is converted from barotropic to baroclinic (baroclinic to barotropic) waves. The main conclusions are: 1) $V_g(x)$ changes the effective frequency $f_{eff}$. This has a direct impact on the slope of the IT characteristics and the slope criticality, which affects the total conversion rate; 2) Since $(V_g)_x$ is not a constant value, $f_{eff}$ varies along the slope. This has a significant effect on the IT beam generation location and its propagation path. If the current is strong enough so that $f_{eff}$ is greater than the barotropic tidal frequency $\sigma_T$, a blocking region is formed where the conversion vanishes and IT propagation is blocked; 3) Changes of sign in $\bar{C}(x,t)$ correspond to the locations where IT beams reflect from the boundaries. As a result, the total conversion rate $C$ is also strongly affected by the IT beam pattern. In conclusion, the total conversion rate $C$ is affected by a combination of three factors: slope criticality, size and location of the blocking region and the IT beam patterm, all of which can be varied by changing the strength, width and location of the geostrophic current $V_g(x)$.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".