Estimating diffusivity from the mixed layer heat and salt balances in the North Pacific
Bibliographic record
Abstract
Data from two National Oceanographic and Atmospheric Administration (NOAA) surface moorings in the North Pacific, in combination with data from satellite, Argo floats and glider (when available), are used to evaluate the residual diffusive flux of heat across the base of the mixed layer from the surface mixed layer heat budget. The diffusion coefficient (i.e., diffusivity) is then computed by dividing the diffusive flux by the temperature gradient in the 20-m transition layer just below the base of the mixed layer. At Station Papa in the NE Pacific subpolar gyre, this diffusivity is 1 × 10−4 m2/s during summer, increasing to ∼3 × 10−4 m2/s during fall. During late winter and early spring, diffusivity has large errors. At other times, diffusivity computed from the mixed layer salt budget at Papa correlate with those from the heat budget, giving confidence that the results are robust for all seasons except late winter-early spring and can be used for other tracers. In comparison, at the Kuroshio Extension Observatory (KEO) in the NW Pacific subtropical recirculation gyre, somewhat larger diffusivities are found based upon the mixed layer heat budget: ∼ 3 × 10−4 m2/s during the warm season and more than an order of magnitude larger during the winter, although again, wintertime errors are large. These larger values at KEO appear to be due to the increased turbulence associated with the summertime typhoons, and weaker wintertime stratification. This article is protected by copyright. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".