The Probability Distribution of the Thorpe Displacement within Overturns in Juan de Fuca Strait
Bibliographic record
Abstract
Vertical mixing in the ocean can sometimes be quantified by measurements of the Thorpe overturning scale, LT. In regions of weak mixing and weak density gradients such measurements may be limited by slow sensor response times (or sampling rates) and/or by lack of resolution and noise in the density measurements. On the other hand, the Thorpe scale can be written as LT = (∫∞0 L2P1(L) dL)1/2, where P1(L) is the probability of the Thorpe displacement, L. Data from Juan de Fuca Strait, British Columbia, show that, even though the probability of a small Thorpe displacement is much greater than that of a large Thorpe displacement, it is the large and more easily resolved values of L that dominate the Thorpe scale. It is found to be possible to determine LT down to a scale of 0.4 m with a conventional conductivity–temperature–depth instrument. This corresponds to values of Kυ ≃ 10−4 m2 s−1 in summertime if LT ≈ (ϵ/N3)1/2, as is confirmed using velocity and temperature microstructure data. Here P1(L) is a convolution of the probability distribution of overturn height, P2(H), with the probability distribution of the fractional displacement within each overturn, P3(L/H). Data show that P2(H) is dominated by small overturns, consistent with previous work on the thickness of turbulence patches. Finally, the distribution of P3(L/H) is examined and compared with the prediction of a very simple kinematic model. The data show a pattern similar to that predicted by the model, though with more small L/H and fewer medium to large L/H than in the model.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".