Assessment of Turbulent Mixing Associated With Eddy‐Wave Coupling Based on Autonomous Observations From the Arctic Canada Basin
Bibliographic record
Abstract
Abstract Interaction between mesoscale eddies and near‐inertial internal waves can contribute to enhanced turbulence mixing but quantitative knowledge from in situ observations is still lacking. This study reveals how eddy/near‐inertial wave interactions (ENIs) can affect the variability of turbulent mixing in the ice‐covered Canada Basin of the Arctic Ocean. We use data from five Ice‐Tethered Profiler with Velocity (ITP‐V) systems that autonomously obtained vertical profiles of horizontal velocity as well as temperature and salinity, which enabled quantification of ENI‐caused turbulent mixing using a fine‐scale parameterization. From the ITP‐V observations in 2013–2015, 67 anticyclones were detected, of which 90% had a deep core at 150–250 m depth. The remaining eddies had a shallow core, typically embedded in the Pacific Summer Water (PSW). Just over one third of the eddies showed evidence of ENI with enhanced near‐inertial internal wave amplitude (NIW) near the eddy cores. For these ENI cases, the parameterized turbulence dissipation rate was O (10−10–10−8 W kg−1), the larger estimates being several orders of magnitude greater than the background level. For the deep eddies, the ENI process can largely be accounted for by the classical theory, NIWs are trapped inside the negative relative vorticity core of anticyclones. For one shallow eddy, the NIW signal was greatest below the core. We postulate that a vertically elongated system of NIWs cannot be constrained vertically within such small‐cored eddies. It is also interpreted that the wave enhancement below the core was supported by the isopycnal slope near the PSW through its geostrophic shear.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".