Near‐inertial waves in <scp>L</scp>ake <scp>E</scp>rie
Bibliographic record
Abstract
Abstract Near‐inertial (Poincaré) waves with a period Tp ∼ 17 h are the dominant wind‐induced internal wave motions in central Lake Erie and consequently have a substantial influence on lake circulation, mixing and biogeochemistry. However, due to the complex three‐basin bathymetry in Lake Erie, the vertical and horizontal modal structure of these waves remain poorly understood. In this study, we analyze field data to show wind events energize frequent vertical mode‐one Poincaré waves. The horizontal modal structure was also investigated, in a sensitivity analysis, using a calibrated three‐dimensional hydrodynamic transport model forced with observed and idealized spatially uniform wind events. Strong horizontal mode‐one Poincaré wave cells form in both the Central and Eastern Basins when wind events have a duration of 0.25 Tp to 0.5 Tp, are impulsive and periodic at Tp, or have anticyclonic rotation with a duration of Tp. Momentum transfer from longer wind events (> 0.5 Tp) will oppose the Coriolis‐force rotated currents and damp Poincaré wave generation. In agreement with theory, the most efficient wind events are observed and computationally modeled to have a duration of 0.25 Tp; causing an excitation peak at ∼ 0.4 Tp and converting ∼ 0.8% of the wind energy input to Poincaré waves. The efficiency of wind work in generating Poincaré wave kinetic energy is given by (1‐cos (2πf t)) t−1, where f is the inertial frequency and t is the wind duration. Therefore, the efficiency peaks during each nTp period, where n is a non‐negative integer, and decreases significantly for longer wind events.
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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) | 0.001 | 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".