Estuarine and Tidal Currents in the Broughton Archipelago
Bibliographic record
Abstract
Current meter observations, Conductivity‐Temperature‐Depth (CTD) profiles, and river discharges are combined with two numerical models to understand better the estuarine and tidal circulation in the Broughton Archipelago, a complex region of islands, channels, and fiords that has become a primary location for salmon farms in British Columbia. Though tidal currents are strong in many sub‐regions, the primary transport mechanisms in the archipelago are the estuarine flows resulting from river and glacial runoff, and the near‐surface currents that arise from strong winds. The harmonic finite element model, TIDE3D, is shown to reproduce the barotropic tidal currents with reasonable accuracy, but unlike other regions of the British Columbia coast where an extensive archive of historical observations has permitted a diagnostic calculation of average seasonal flows, sparse and noisy CTD observations did not allow a similar computation here. In order to simulate these background flows, the prognostic finite volume model, ELCIRC, was initialized with a smoothed version of these same historical temperature and salinity fields and forced with tides and river discharge. Though the near‐surface flows were reproduced with acceptable accuracy, the estuarine and tidal flows at depth were found to be much too weak as a result of numerical dissipation arising from the Eulerian‐Lagrangian time stepping. Nevertheless, ELCIRC did confirm current observations suggesting that the bottom estuarine flow in Knight Inlet actually comes from Queen Charlotte Strait via the “back‐door” of Fife Sound and Tribune Passage. Consistent with anecdotal evidence, ELCIRC also showed that the surface estuarine flow coming down Knight Inlet bifurcates with part going down Tribune Channel and Fife Sound and part continuing down Knight Inlet. The relevance of these background flows for aquaculture issues, such as oxygen renewal and the transport of sea lice and viruses, is discussed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".