Turbulent diffusivity profiles on the shelf and slope at the southern edge of the Canada Basin
Bibliographic record
Abstract
Vertical profiles of temperature microstructure at 95 stations were obtained over the Beaufort shelf and shelfbreak in the southern Canada Basin during a November 2018 research cruise. Two methods for estimating the dissipation rates of temperature variance and turbulent kinetic energy were compared using this dataset. Both methods require fitting a theoretical spectrum to observed temperature gradient spectra, but differ in their assumptions. The two methods agree for calculations of the dissipation rate of temperature variance, but not for that of turbulent kinetic energy. After applying a rigorous data rejection framework, estimates of turbulent diffusivity and heat flux are made across different depth ranges. The turbulent diffusivity of temperature is typically enhanced by about one order of magnitude in profiles on the shelf compared to near the shelfbreak, and similarly near the shelfbreak compared to profiles with bottom depth >1000 m. Depth bin means are shown to vary depending on the averaging method (geometric means tend to be smaller than arithmetic means and maximum likelihood estimates). The statistical distributions of heat flux within the surface, cold halocline, and Atlantic water layer change with depth. Heat fluxes are typically <1 Wm−2, but are greater than 50 Wm−2 in ∼8% of the overall data. These largest fluxes are located almost exclusively within the surface layer, where temperature gradients can be large.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".