Improving Water Quality in a Sea Bay by Connecting Rivers on Both Sides of a Harbor
Bibliographic record
Abstract
Improving water quality is imperative for many sea bays, including Laizhou Bay, China, to achieve sustainable marine development. In 2010, two 17.3 km long sand-retaining embankments were built in southwestern Laizhou Bay, which weakened the nearshore hydrodynamics and seriously impacted the water quality. To mitigate this issue, this study proposes connecting the two rivers on both sides of the embankments to improve the hydrodynamics and thus water exchange in the sea bay. The effectiveness was examined with a numerical model using Mike 21, which was validated for both tidal current velocity and direction at six monitoring locations in the sea bay. The results show that over 53% of the core research area displays an increase (0.0–0.4 m/s) in tidal current speed after the connection, primarily in and around the Haihengwei Fishing Port. Meanwhile, the Eulerian residual currents in the Haihengwei Fishing Port, Mi River estuary and Bailang River estuary become substantially larger (with a maximum increase of 0.16 m/s). In addition, the net transport distance of particles released near the connection increases by up to 39.89 km in one month. Overall, this case study demonstrates that connecting rivers next to a harbor can effectively improve hydrodynamics and thus improve water quality in the bay.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".