Vertical heat transfer based on direct microstructure measurements in the ice-free Pacific-side Arctic Ocean: the role and impact of the Pacific water intrusion
Bibliographic record
Abstract
This study quantifies diapycnal mixing and vertical heat transfer in the Pacific side of the Arctic Ocean, where sea-ice cover has disappeared between July and September in the last few decades. We conducted microstructure measurements in the open water region around the Canada Basin from late summer to fall in 2009 and 2010 using R/V Mirai. In the study domain, the dissipation rate of turbulent kinetic energy, e, is typically as low level as O(10−10) W kg−1, resulting in vertical heat diffusivity of O(10−7) m2 s−1, which is close to the molecular diffusivity of heat, suggesting comparatively little predominance of mechanical turbulent mixing. An exception is the case at the Barrow Canyon, where the strong baroclinic throughflow generates substantial vertical mixing, producing e > O(10−7) W kg−1, because of the shear flow instability. Meanwhile, in the confluence region, where the warm/salty Pacific water and the cold/fresh Arctic basin water encounter, the micro-temperature profiles revealed a localized enhancement in vertical diffusivity of heat, reaching O(10−5) m2 s−1 or greater. In this region, an intrusion of warm Pacific water creates a horizontally interleaved structure, where the double-diffusive mixing facilitates vertical heat transfer between the intruding Pacific water and the surrounding basin waters.
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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 | 0.000 | 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) | 0.000 | 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".