Measurement and numerical modeling studies of the highest bottom shear stress in the Randle Reef area
Bibliographic record
Abstract
The purpose of this study was to investigate the highest bottom shear stress, induced by wind in an area of Hamilton Harbour, Ontario, Canada known as Randle Reef. The study was conducted in support of a component of a contaminated sediment remediation plan utilizing a thin layer of sand to manage contaminated sediments. Toward this end, four acoustic Doppler current profilers (ADCPs) were deployed at two locations in the study region to measure velocity profiles for the purpose of indirectly measuring bottom shear stress (BSS) and model verification. There is no easy way to directly measure BSS in the field. As a result, the use of the logarithmic-profile method from the ADCP measured high resolution velocity profiles in the bottom layer was explored. This approach, according to our best knowledge, has not been published for a wind driven flow in a small open water body. To use the indirectly measured BSS to estimate the highest BSS in the study area, a three-dimensional hydrodynamic model was adopted to provide the spatial and temporal information of the bottom flow. The results showed that the modeled and measured flow velocity components agreed reasonably well at most of the water depths with the correlation coefficients being greater than 0.6. However, agreements between the modeled and measured bottom flow speeds were worse than expected due to the error contributions from both the modeled velocity components. Therefore, the modeled flow speed required rescaling based on ADCP velocity measurements before it could be deemed reliable. This is especially important in estimation of the BSS with a quadratic formula because the calculated BSS is proportional to the square of the speed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".