Virtual simulations of potential vessel discharges in Puget Sound and the Puget Sound No Discharge Zone
Bibliographic record
Abstract
The Washington State Department of Ecology (Ecology) evaluated the potential transport, dispersion and dilution of potential vessel sewer discharges within the draft proposed Puget Sound No Discharge Zone (NDZ). These model simulations included potential vessel sewer discharges at six locations in Puget Sound along major shipping routes. Results are presented as virtual animations of surface concentrations, allowing us to visualize the transport, circulation, and dilution of these discharges over the course of several days. Ecology and Pacific Northwest National Laboratory jointly developed a three-dimensional hydrodynamic FVCOM (Finite Volume Coastal Ocean Model) computer model of the Salish Sea. This model is forced by tides at the mouth of the Strait of Juan de Fuca, meteorological boundary conditions, and freshwater inputs from the US and Canada that induce estuarine circulation. The model has been calibrated to water surface elevations and profiles for the year 2006. Potential discharges were simulated using the sediment module of the model, with zero settling velocity. The model allows us to evaluate connectivity between points of discharge and other parts of Puget Sound, and calculate the degree of dilution that has occurred before discharges reach specific geographic points of interest. Model simulations show the strong influence of tidal cycles on the movement of vessel sewer discharge. At some locations, freshwater inflows into Puget Sound from rivers also influence the movement and dilution. We found that there are several periods when fecal coliform bacteria concentrations typical of raw sewage would not meet the water quality standard based on dilution alone. Since its development, Ecology has used the model for a number of applications to evaluate how potential point and nonpoint source discharges (e.g. wastewater facilities, vessel sewage discharges, and rivers) circulate once released into marine waters.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".