Bay- and Inlet-Scale Hydrodynamics in a Back-Barrier System During Storms
Bibliographic record
Abstract
Hydrodynamics in shallow, large, back-barrier bays are spatiotemporally variable owing to bay geometry, presence of inlets, and storm forcing conditions. We investigate the relative contributions of winds and ocean waves to hydrodynamics in the Albemarle-Pamlico Sound, rimmed by the Outer Banks of North Carolina, USA, during storm events. These events include Hurricane Isaias (August 2020) and a Nor’easter combined with the remnants of Hurricane Teddy (September 2020). The hydrodynamics on a bay scale O(100,000 m) across the Sound and on an inlet scale O(100 m) at Oregon Inlet are investigated using field observations and numerical simulations. Winds create a water level gradient across the north-south axis of the bay, contributing to local water level gradients in both the along- and cross-inlet directions at the inlet. The strength of wind impacts depends on wind speed, direction, and barrier island geometry. Ocean waves have bay-scale impacts of driving a nearly-uniform increase in water levels throughout the Sound that depends on wave height, direction, and storm duration. Local to the inlet, wave impacts vary spatiotemporally with tidal phase and bathymetry. Currents through the inlet are typically wind-dominated, with wave impacts becoming important only over longer storm durations. The novel combination of field observations with numerical models at different scales reveals the detailed storm-driven response of a major inlet that connects a large estuary to the ocean, with implications for understanding the hydrodynamic responses of other large back-barrier bay-inlet systems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".