Nonlinear interactions of waves and tides in a subterranean estuary
Bibliographic record
Abstract
Abstract Previous studies have revealed hysteretic behavior in subterranean estuaries in response to intensified wave conditions caused by offshore storm events, showing dependence of submarine groundwater discharge (SGD) and subsurface salt distribution on historic wave conditions. Although most shorelines worldwide are also exposed to tidal fluctuations, it is unclear how tides moderate wave‐induced SGD and salinity distribution in subterranean estuaries. This study presents numerical simulations that explore the combined influence of intensified wave conditions and tides on groundwater flow and salt transport in a subterranean estuary. The results show that tides weaken the hysteretic wave effect on SGD, suggesting that a tidally influenced subterranean estuary is less sensitive to intensified wave conditions with respect to the water fluxes across the aquifer‐sea interface. However, due to enhancement of salt‐freshwater mixing, tides strengthen the hysteretic wave effect on the salt fluxes across the aquifer‐sea interface, prolonging the recovery of salt distribution in the subterranean estuary to the prestorm state. These findings reveal the nonlinear, coupling nature of processes driven by oceanic oscillations at different time scales in subterranean estuaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".