Monitoring suspended sediment plumes by optical and acoustical methods with application to sand capping
Bibliographic record
Abstract
There is a need to rapidly survey plumes of suspended sediment and to determine the quantity and transport of disturbed material that might arise from industrial and remedial activities in lakes and harbours. The example of sand capping of contaminated bottom sediments is used to illustrate the methodology. Capping of contaminated bottom sediments is one strategy for immobilizing contaminants. In a pilot-scale project, a 0.34 m thick cap of clean sand was deposited over a one hectare area in Hamilton Harbour. The objective of this study was to determine if in-place sediments could be disturbed by the capping operation. In the first of three approaches, the suspended sediment plumes created during the capping operation were monitored for evidence of suspension of bottom materials with underwater acoustic and optical profilers and other moored instruments. In-lake calibration of the instrumentation was based on the collection of over 300 grab samples. Three-dimensional rendering of a dense network of acoustic backscatter profiles revealed that there was no evidence that bottom sediment was resuspended. The density current flowing downslope close to the bottom caused by the capping material was examined and found too weak to erode bottom sediments. Finally, a quantitative estimation of the amount of sediment suspended in the plumes indicated little evidence for resuspension of in-place sediments. The methodology developed could also be applied to estimate the concentration and quantities of contaminants in sediments suspended by other remedial activities such as dredging. Key words: suspended sediment plumes, underwater acoustics, optical measurements, sediment remediation, harbours, lakes.
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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 | 0.001 |
| 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) | 0.000 | 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".