Seasonal and interannual Salish Sea inflow origins using Lagrangian tracking
Bibliographic record
Abstract
The Salish Sea is a semi-enclosed sea between Vancouver Island and the coast of British Columbia and Washington State, invaluable from both an economic and ecologic perspective. Here we explore the contribution of Pacific water masses to the flow through Juan de Fuca Strait (JdF), the Salish Sea’s primary connection to the Pacific Ocean. Quantitative Lagrangian particle tracking within Ariane, an offline Lagrangian tool capable of volume transport calculations, was applied to two numerical ocean models to track the paths and physical properties of water parcels before entering JdF (CIOPS) and within the Salish Sea (SalishSeaCast). During summer upwelling, flow from the north shelf and offshore dominate Pacific inflow, while during winter downwelling, flow from the south shelf and surface flow from the Columbia River plume are the dominant Pacific sources. A weaker and less consistent estuarine flow regime in the winter leads to less Pacific inflow overall and a smaller percentage of said inflow reaching the Salish Sea’s inner basins than in the summer. Nevertheless, it was found that winter dynamics are a large driver of interannual variability, in part due to the anti-correlated behaviour and distinct properties of the two dominant winter sources. This analysis extends the knowledge on the dynamics of Pacific inflow to the Salish Sea and highlights the importance of winter inflow to interannual variability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".