Poincaré wave–induced mixing in a large lake
Bibliographic record
Abstract
A 10,000‐km 2 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".