Seasonably Variable Estuarine Exchange Through Interconnected Channels in the Salish Sea
Bibliographic record
Abstract
Abstract The Salish Sea is a semi‐enclosed estuary whose largest basin (Strait of Georgia (SoG)) is connected to the north‐eastern Pacific Ocean through regions with tight constructions and sills that cause intense tidal mixing. The estuarine circulation is complicated through the tidally mixed region around the San Juan and Gulf Islands, which consists of three different straits: Haro Strait, Rosario Strait, and San Juan Channel (SJC). Haro Strait, as the largest and deepest of the channels, is the dominant pathway; however, we determine that Rosario Strait also has an important influence in this region. To examine the differences in water transport through the different channels, Lagrangian particle tracking experiments were performed for a 4‐year hindcast (from 2018 to 2022) using the 3‐dimensional numerical model SalishSeaCast. While Haro Strait has southward surface flow and northward deep flow, the flux making it through Rosario Strait and SJC is primarily southward. The proportion of the total southward flux through these two channels is higher from May to October and this increase is attributed to the influence of both the Fraser River and the river discharge from Puget Sound. Rosario Strait is the dominant pathway of southward exchange from the SoG to Puget Sound, while the majority of deep northward flux to the SoG is through Haro Strait.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".