Measurement of Longshore Sediment Transport Rates in the Surf Zone on Galveston Island, Texas
Bibliographic record
Abstract
Longshore sediment transport in the surf zone on Galveston Island, Texas, was studied to develop a new technique involving optical instruments rapidly calibrated in situ and to compare measured transport rates with those predicted by the well-known Coastal Engineering Research Center (CERC) formula. This method used an instrumented sled equipped with a LISST-100 for particle size distribution determination, four optical backscatter sensors (OBSs) for turbidity measurements, and two velocity sensors for longshore current measurements. The sled was pulled across the surf zone, occupying 10 to 15 stations spaced about 10 m apart, for approximately 3 minutes each.The OBS data were calibrated with the LISST-100 particle size distribution data, thereby overcoming the difficulties associated with the use of these sensors in the presence of a mix of sand and fine particles. Subsequently, these data were fit to a logarithmic profile to determine the average vertical distribution of suspended sand concentration, assuming that the fine particles were vertically well mixed at each station. A logarithmic profile of average longshore current was also computed based on the measured velocity data. The longshore sediment transport rate was calculated as the spatial integral of the product of suspended sand concentration and velocity and related to the wave conditions at the point of breaking. Measured rates ranged from 86,000 to 231,000 m3/y, and transport was found to be greatest in the vicinity of the sand bars. The popular CERC formula gave sediment transport rates significantly greater than the observed values, with the difference between the two on the order of 100%. The average CERC transport coefficient, K1, computed from our measurements was determined to be 0.19 ± 0.12.
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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) | 0.001 | 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".