Nearshore-offshore exchanges by enhanced turbulent mixing along the north shore of Lake Ontario
Bibliographic record
Abstract
Seasonal nearshore-offshore exchanges by coastal upwelling events in large lakes can play a significant role in nearshore nutrient dynamics, affecting lake productivity and water quality. We analyzed field observations along the north shore of Lake Ontario, collected in the summer of 2018, focusing on the littoral zone and specifically the Cladophora habitat zone (<15 m), to investigate episodic enhanced vertical mixing by coastal up/downwelling events. Vertical turbulent diffusivity (Kz) based on the buoyancy Reynolds number above the metalimnion layer during downwelling events was generally higher than those below the metalimnion layer during upwelling events; while Kz at the metalimnion layer can increase by ∼ two orders of magnitude during upwelling events. Our results suggest that Kz based on the Richardson number parameterization, which only accounts for large-scale current shear and stability, and incorporates an adjustable parameter is ∼ ten times higher than the Kz based on the buoyancy Reynolds number. Analysis of historical wind records indicates that the frequency of coastal upwelling favorable winds on the north shore of Lake Ontario has increased by > 45% over the last thirty years - suggesting an increasing trend of nearshore-offshore nutrient exchanges as a contributing factor for the nearshore water quality management.
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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 | 0.001 | 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) | 0.001 | 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".