Observation and Parameterization of Bottom Shear Stress and Sediment Resuspension in a Large Shallow Lake
Bibliographic record
Abstract
Abstract Parameterizations for bottom shear stress are required to predict sediment resuspension from field observations and within numerical models that do not resolve flow within the viscous sublayer. This study assessed three observation‐based bottom shear stress ( τ b ) parameterizations, including (a) the sum of surface wave stress and mean current (quadratic) stress ( ); (b) the log‐law ( τ b = τ L ); and (c) the turbulent kinetic energy ( τ b = τ TKE ); using 2 years of observations from a large shallow lake. For this system, the parameterization τ b = τ w + τ c was sufficient to qualitatively predict resuspension, since bottom currents and surface wave orbitals were the two major processes found to resuspend bottom sediments. However, the τ L and τ TKE parameterizations also captured the development of a nepheloid layer within the hypolimnion associated with high‐frequency internal waves. Reynolds‐averaged Navier‐Stokes (RANS) equation models parameterize τ b as the summation of modeled current‐induced bottom stress ( τ c , m ) and modeled surface wave‐induced bottom stress ( τ w , m ). The performance of different parameterizations for τ w , m and τ c , m in RANS models was assessed against the observations. The optimal parameterizations yielded root‐mean‐square errors of 0.031 and 0.025 Pa, respectively, when τ w , m , and τ c , m were set using a constant canonical drag coefficient. A RANS‐based τ L parameterization was developed; however, the grid‐averaged modeled dissipation did not always match local observations, leading to O (10) errors in prediction of bottom stress. Turbulence‐based parameterizations should be further developed for application to flows with mean shear‐free boundary turbulence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".