Global Observations of Rotary-With-Depth Shear Spectra
Bibliographic record
Abstract
Abstract Internal waves are predominantly generated by winds, tide/topography interactions and balanced flow/topography interactions. Observations of vertical shear of horizontal velocity (uz, vz) from LADCP profiles conducted during GO-SHIP hydrographic surveys, as well as vessel-mounted sonars, are used to interpret these signals. Vertical directionality of intermediate-wavenumber [λz ~ 𝒪(100 m)] internal waves is inferred in this study from rotary-with-depth shears. Total shear variance and vertical asymmetry ratio (Ω), i.e. the normalized difference between downward- and upward-propagating intermediate wavenumber shear variance, where Ω > 0 (< 0) indicates excess downgoing (upgoing) shear variance, are calculated for three depth ranges: 200-600 m, 600 m to 1000 mab (meters above bottom), and below 1000 mab. Globally, downgoing (clockwise-with-depth in the northern hemisphere) exceeds upgoing (counterclockwise-with-depth in the northern hemisphere) shear variance by 30% in the upper 600 m of the water column (corresponding to the globally averaged asymmetry ratio of = 0.13), with a near-equal distribution below 600-m depth ( ~ 0). Downgoing shear variance in the upper water column dominates at all latitudes. There is no statistically significant correlation between the global distribution of Ω and internal wave generation, pointing to an important role for processes that re-distribute energy within the internal wave continuum on wavelengths of 𝒪(100 m).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".