The effects of the spatial distribution of bottom topography and bottom drag on seiche-induced wave train formation
Bibliographic record
Abstract
We consider the nonlinear, non-hydrostatic dynamics of seiches in small to medium-sized lakes. Using numerical simulations of shallow water equations modified to include weakly non-hydrostatic effects, we illustrate how spatially varying bottom drag and finite amplitude topography lead to the bending of wave trains that develop from the initial standing wave. For the case of variable topography, we discuss how the seiche and the wave trains that develop can resuspend material (e.g. nutrients) from the bottom of the lake and redistribute it in space. The numerical methods employed are spectrally accurate in space and second-order in time, yielding excellent accuracy and little numerical dissipation. We find that while the resuspension itself is largely due to the long standing waves at early times, the redistribution of nutrient distribution that is seen at later times is profoundly influenced by the development of the wave trains; a fundamentally non-hydrostatic effect.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".