Temporal Variability of Internal Wave‐Driven Mixing in Two Distinct Regions of the Arctic Ocean
Bibliographic record
Abstract
Abstract This work investigates how internal wave‐driven turbulence varies in time, from hourly to yearly timescales, and in space, across two distinct regions of the Arctic Ocean. We apply a shear‐based fine‐scale parameterization to mooring records in Nares Strait and on the Beaufort Sea shelf‐slope that sampled the upper stratified water column every 30–45 min and span 2003–2006 and 2003–2004, respectively. In doing so, we generate over 600,000 estimates of the internal wave‐driven dissipation rate. These estimates exhibit large temporal variability in both regions, spanning over 3 orders of magnitude. Despite these wide ranges, we find distinct distributions at each site. In Nares Strait, the time series of dissipation shows systematic variation at tidal frequencies, and tidal forcing appears to influence dissipation more strongly than winds, sea ice, and stratification on daily timescales. On longer timescales, dissipation exhibits a weak seasonal cycle, being elevated when the stratification is high and during the ice melt season. In the Beaufort Sea, we detect no dominant timescales or significant relationships with forcing metrics, but note that the dissipation rate is typically 2 orders of magnitude lower than that in Nares Strait. This region is characterized as being in a turbulent mixing regime for only 2% of the record, compared to 73% of the Nares Strait record, implying that turbulence here is rarely energetic enough relative to the stratification to drive a turbulent heat flux. Inferred Beaufort Sea heat fluxes are an order of magnitude lower than the O(1) W m−2 average value found in Nares Strait.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".