Wind-driven upwelling and surface nutrient delivery in a semi-enclosed coastal sea
Bibliographic record
Abstract
Abstract. Wind-driven upwelling is an important control on surface nutrients and water properties in stratified lakes and seas. In this study, a high-resolution biophysical coupled model is used to investigate upwelling in the Strait of Georgia on the Canadian Pacific coast. The model is forced with surface winds from a high-resolution atmospheric forecast and has been tuned in previous studies to reproduce extensive observations of water level, temperature, salinity, nutrients and chlorophyll with competitive skill relative to similar models of the study region. A total of 5 years of hourly surface nitrate and temperature fields are analyzed in order to characterize the dominant upwelling patterns of the basin. A prevailing along-axis wind pattern steered by mountainous topography produces episodic upwelling along the western shore during the spring and fall southeasterlies and along the eastern shore during the summer northwesterlies, as indicated by positive nitrate anomalies. Principal component analysis reveals that these cross-axis upwelling patterns account for nearly one-third of the surface nitrate variance during the summer productive season. By contrast, nearly half of the surface temperature variance over the same period is dominated by a single, combined mixing and diurnal heating–cooling pattern. The principal components associated with these patterns correlate with along-axis wind stress in a manner consistent with these physical interpretations. The cross-axis upwelling response to wind is similar to other dynamically wide basins where the baroclinic Rossby deformation radius is smaller than the basin width. However, the nitrate anomaly during upwelling along the eastern shore is stronger in the northern basin, which may be indicative of an along-axis pycnocline tilt or an effect of the background along-axis stratification gradient due to the Fraser River. Our findings highlight an important spatiotemporal consideration for future ecosystem monitoring.
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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 | 0.000 | 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) | 0.000 | 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".