Wave-induced horizontal diffusivity from optically sensed dye tracer fields in impermeable beach laboratory experiments
Bibliographic record
Abstract
Contaminants in nearshore regions can have negative consequences for aquatic life, public health, and the economic value of beaches. The associated risk in these regions depends on the relative concentrations of the contaminant at different distances from the shore. To address this concern, we performed passive tracer studies during a series of experiments in a laboratory wave basin, releasing dye near-instantaneously into the swash zone and outside of the breaking zone under monochromatic waves of varying heights and incident angles. By tracking dye patch evolution with cameras, we approximated horizontal diffusivity of the dye from the time rate of change of its variance in the cross-shore and alongshore directions. We performed approximately 50 dye release experiments with a combination of three wave heights and three wave angles. From these experiments, we approximate cross-shore and alongshore diffusivities ( κ x , κ y ) and explore parameterizations of these diffusivities on the basis of cross-shore location and wave conditions. The results indicate an order of magnitude increase in both κ x and κ y from the region of wave shoaling to the surf and swash zones. The nearshore diffusivity estimates show good agreement with previous empirical models and values reported in the literature, and for the first time provide insight on the detailed cross-shore distribution of horizontal diffusivity inside and outside of the wave breaking region.
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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 | 0.001 |
| 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 | 0.000 | 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".