Bibliographic record
Abstract
Abstract Using a set of models, including one with a resolution of ¼°, several aspects of the simulated seasonal currents in the deep ocean are considered. It is shown that over vast areas of the deep interior, particularly in the Indian Ocean, annual-mean circulation represents a small residual of much stronger seasonal flows. In many places the seasonal horizontal velocities are of the order of 10−2 m s−1, reaching locally to 10−1 m s−1; the corresponding vertical velocities are of the order of 10−5 m s−1. An idealized geometry model is employed to confirm the notion that much of this seasonal variability in the deep-ocean circulation can be attributed to the annual cycle of wind stress, combined with the significant increase in the vertical trapping depth for basin-scale seasonal forcing. It is suggested that, at least on seasonal time scales, the so-called bottom pressure torque can be an important term in the depth-integrated vorticity balance. An interaction of these relatively strong flows (of nontidal origin) with bottom topography may contribute to diapycnal mixing in the deep ocean in a manner similar to that proposed recently for the Southern Ocean. In addition, it is found that under a plausible climate change scenario, the amplitude of the mean annual cycle of wind stress may change. Among the regions where such changes are most pronounced is that in the extratropical North Pacific. It is shown that the data on surface wind stress can be effectively used to identify the seasons with the largest changes in the deep-reaching overturning cells. Finally, unlike what might be expected from the earlier theories, the annual-mean circulation simulated by the model with ¼° resolution has the deep interior flows that tend to group into jetlike structures, often having a predominant equatorward rather than poleward direction.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".