Turbulent mixing and hydraulic control of abyssal water in the Samoan Passage
Bibliographic record
Abstract
We report the first direct turbulence observations in the Samoan Passage (SP), a 40 km wide notch in the South Pacific bathymetry through which flows most of the water supplying the North Pacific abyssal circulation. The observed turbulence is 1000 to 10,000 times typical abyssal levels —strong enough to completely mix away the densest water entering the passage—confirming inferences from previous coarser temperature and salinity sections. Accompanying towed measurements of velocity and temperature with horizontal resolution of about 250 m indicate the dominant processes responsible for the turbulence. Specifically, the flow accelerates substantially at the primary sill within the passage, reaching speeds as great as 0.55 m s −1 . A strong hydraulic response is seen, with layers first rising to clear the sill and then plunging hundreds of meters downward. Turbulence results from high shear at the interface above the densest fluid as it descends and from hydraulic jumps that form downstream of the sill. In addition to the primary sill, other locations along the multiple interconnected channels through the Samoan Passage also have an effect on the mixing of the dense water. In fact, quite different hydraulic responses and turbulence levels are observed at seafloor features separated laterally by a few kilometers, suggesting that abyssal mixing depends sensitively on bathymetric details on small scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".