The role of wind in determining the timing of the spring bloom in the Strait of Georgia
Bibliographic record
Abstract
A coupled biophysical model of the Strait of Georgia (SoG), British Columbia, Canada, has been developed and successfully predicts the timing of the spring phytoplankton bloom. The physical model is a one-dimensional vertical mixing model, using a K-profile parametrization of the boundary layer, forced with high frequency meteorological data. The biological model includes one phytoplankton class (microphytoplankton) and one nutrient source (nitrate). The spring bloom in the SoG occurs when phytoplankton receive enough light that their growth rates exceed their loss rates. The amount of light that the phytoplankton receive is a function of solar radiation and the depth of mixing. The model was used to determine what physical factors are controlling the phytoplankton losses and the light received by the phytoplankton. Wind was found to control the spring bloom arrival time, with strong winds increasing the mixing-layer depth and delaying the bloom. The amount of incoming solar irradiance, through amount of cloud cover, had a secondary effect. The freshwater input (primarily Fraser River discharge) had an insignificant effect on the timing. Increased freshwater flux increases the buoyancy flux and thus decreases the mixing-layer depth but also increases the strength of the estuarine circulation, increasing the advective loss.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".