Bibliographic record
Abstract
The method used to separate surface and internal tides ultimately defines properties such as internal‐tide generation and the depth structure of internal‐tide energy flux. Here, we provide a detailed analysis of several surface‐/internal‐tide decompositions over arbitrary topography. In all decompositions, surface‐tide velocity is expressed as the depth average of total velocity. Analysis indicates that surface‐tide pressure is best expressed as the depth average of total pressure plus a new depth‐dependent profile of pressure, which is due to isopycnal heaving by movement of the free surface. Internal‐tide velocity and pressure are defined as total variables minus the surface‐tide components. Corresponding surface‐ and internal‐tide energy equations are derived that contain energy conversion solely through topographic internal‐tide generation. The depth structure of internal‐tide energy flux produced by the new decomposition is unambiguous and differs from that of past decompositions. Numerical simulations over steep topography reveal that the decomposition is self‐consistent and physically relevant. Analysis of observations over Kaena Ridge, Hawaii; and the Oregon continental slope indicate O (50 W m−1) error in depth‐integrated energy fluxes when internal‐tide pressure is computed as the residual of pressure from its depth average. While these errors are small at major internal‐tide generation sites, they may be significant where surface tides are larger and depth‐integrated fluxes are weaker (e.g., over continental shelves).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".