On using the finescale parameterization and Thorpe scales to estimate turbulence from glider data
Bibliographic record
Abstract
Abstract Turbulence plays a key role in many oceanic processes, but a shortage of turbulence observations impedes its exploration. Parameterizations of turbulence applied to readily-available CTD data can be useful in expanding our understanding of the space-time variability of turbulence. Typically tested and applied to shipboard data, these parameterizations have not been rigorously tested on data collected by underwater gliders, which show potential to observe turbulence in conditions that ships cannot. Using data from a 10-day glider survey in a coastal shelf environment, we compare estimates of turbulent dissipation from the finescale parameterization and Thorpe scale method to those estimated from microstructure observations collected on the same glider platform. We find that the finescale parameterization captures the magnitude and statistical distribution of dissipation, but cannot resolve spatiotemporal features in this relatively shallow water depth. In contrast, the Thorpe scale method more successfully characterizes the spatiotemporal distribution of turbulence; however, the magnitude of dissipation is overestimated, largely due to limitations on the detectable density overturn size imposed by the typical glider CTD sampling frequency of 0.5 Hz and CTD noise. Despite these limitations, turbulence parameterizations provide a viable opportunity to use CTD data collected by the multitude of gliders sampling the ocean to develop greater insight into the space-time variability of ocean turbulence and the role of turbulence in oceanic processes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".