Seasonal Wind Stress Direction Influences Source and Properties of Inflow to the Salish Sea and Columbia River Estuary
Bibliographic record
Abstract
Abstract Estuaries in the northern California current system (NCCS) experience seasonally reversing wind stress, which is expected to impact the origin and properties of inflowing ocean water. Wind stress has been shown to affect the source of estuarine inflow by driving alongshelf currents. However, the effects of vertical transport by wind‐driven Ekman dynamics and other shelf and slope currents on inflow are yet to be explored. Variations in inflow to two NCCS estuarine systems, the Salish Sea and the Columbia River estuary, were studied using particle tracking in a hydrodynamic model. Particles were released in a grid extending two degrees of latitude north and south of each estuary every two weeks of 2017 and tracked for sixty days. Inflow was identified as particles that crossed the estuary mouths. Wind stress was compared with initial horizontal and vertical positions and physical properties of shelf inflow particles. Inflow to the Salish Sea came from Vancouver Island and Washington slope water upwelled through canyons during upwelling‐favorable wind stress, and from Washington slope water or Columbia River plume water during downwelling‐favorable wind stress. Inflow to the Columbia River estuary came from Washington shelf bottom water during upwelling‐favorable wind stress and Oregon shelf surface water during downwelling‐favorable wind stress. For both estuaries, upwelling‐favorable wind stress direction was significantly correlated with a denser and deeper shelf inflow source north of the estuary mouth. These results may help predict the source and properties of inflow to estuaries in other regions with known wind or shelf current patterns.
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 | 0.000 | 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".