Assessment of Nutrient Supply by a Tidal Jet in the Northern Strait of Georgia Based on a Biogeochemical Model
Bibliographic record
Abstract
Abstract We present a coupled three‐dimensional biological‐physical model for the Salish Sea and evaluate it by comparison to nitrate, silicate, and chlorophyll observations. It accurately reproduces nitrate concentrations with Willmott skill scores, root‐mean‐square error, and bias ranging from 0.84–0.95, 4.02–6.5 μM, and −2.33–1.84 μM, respectively, compared to three independent discrete sample data sets. A prominent feature of the model output is a tidal jet emanating from Discovery Passage producing a downstream plume of elevated surface nitrate. The signal is present from April to September, when surface nitrate is otherwise drawn down. It has a weak but statistically significant correlation to Discovery Passage tidal velocity (R=0.37, p<0.01). Within the turbulent jet and associated plume, the average rate of vertical nitrate supply due to mixing and advection across a depth of roughly 6 m is 0.46 μmol m−2 s−1 between 15 May and 20 August 2015, compared to 0.10 μmol m−2 s−1 for the northern Strait of Georgia as a whole. Close to Discovery Passage, where velocities and shear are strongest, the majority of the vertical nitrate flux is due to mixing. As velocities weaken downstream, vertical advection becomes more important relative to mixing, but vertical velocities also decrease. The tidal pulses out of Discovery Passage drive waves that contribute net upward nitrate flux as far south as Cape Lazo, 40 km away. The nitrate supply drives new production, consistent with existing observations. Similar dynamics have been described in many other tidally influenced coastal 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".