Control of salinity on the mixed layer depth in the world ocean: 1. General description
Bibliographic record
Abstract
Using instantaneous temperature and salinity profiles, including recent Argo data, a global ocean climatology of monthly mean properties of the “barrier layer” (BL) phenomenon is constructed. This climatology is based on the individual analysis of instantaneous profiles in contrast with previous large‐scale climatologies derived from gridded fields. This ensures a more accurate description of the BL phenomenon. We distinguish three types of regions: BLs are quasi‐permanent in the equatorial and western tropical Atlantic and Pacific, the Bay of Bengal, the eastern equatorial Indian Ocean, the Labrador Sea, and parts of the Arctic and Southern Ocean. In the northern subpolar basins, the southern Indian Ocean, and the Arabian Sea, BLs are rather seasonal. Finally, BLs are typically never detected between 25° and 45° latitude in each basin. Away from the deep tropics, the analysis reveals strong similarities between the two hemispheres and the three oceans regarding BL seasonality and formation mechanisms. Temperature inversions below the mixed layer are often associated with BLs. Their typical amplitude, depth, and seasonality are described here for the first time at global scale. We suggest that this global product could be used as a reference for future studies and to validate the representation of upper oceanic layers by general circulation models.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".