Poincaré wave–induced mixing in a large lake
Bibliographic record
Abstract
A 10,000‐km2 hypoxic ‘dead zone’ forms, during most years, in the central basin of Lake Erie. To investigate the processes driving the hypoxia, we conducted a 2‐yr field campaign where the mixing in the lake interior during the stratification period was examined using current meters and temperature‐loggers data, as well as > 600 temperature microstructure profiles, from which turbulent mixing was computed. Near‐inertial Poincaré waves drive shear instability, generating ∼ 1‐m amplitude and 10‐m wavelength high‐frequency internal waves with ∼ 1‐mdensity overturns that lead to an increase in turbulent dissipation by one order of magnitude. The instabilities are associated with enhanced vertical shear at the crests and troughs of the Poincaré waves and may be correlated with the local gradient Richardson number. Poincaré wave–induced mixing should be an important factor when the Burger number < 0.25. The strong diapycnal mixing induced by the Poincaré wave activity will also significantly modify the energy‐flux paths. For example, we estimate that, in Lake Erie, 0.85% of the wind energy is transferred to the lake interior (below the surface layer); of this, 40% is dissipated in the interior metalimnion and 60% is dissipated at the bottom boundary. In smaller lakes, 0.42% of wind energy is transferred to the deeper water, with 90% dissipated in the boundary and 10% in the interior metalimnion.
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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 | 0.001 |
| 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".