Characteristics of the Exchange Flow of the Bay of Quinte and Its Sheltered Embayments with Lake Ontario
Bibliographic record
Abstract
The nature of the exchange flow between the Bay of Quinte and Lake Ontario has been studied to illustrate the effects of the seasonal onset of stratification on the flushing and transport of material within the bay. Flushing is an important physical process in bays used as drinking water sources because it affects phosphorous loads and water quality. A 2-d analytical model and a 3-dimensional numerical coastal model (FVCOM) were used together with in situ observations of temperature and water speed to illustrate the two-layer nature of the late summer exchange flow between the Bay of Quinte and Lake Ontario. Observations and model simulations were performed for spring and summer of 2018 and showed a cool wedge of bottom water in late summer extending from Lake Ontario and moving into Hay Bay at approximately 3 cm/s. Observed and modelled water speeds were used to calculate monthly averaged fluxes out of the Bay of Quinte. After the thermocline developed, Lake Ontario water backflowed into the Bay of Quinte at a rate approximately equal to the surface outflow decreasing the flushing rate. Over approximately 18.5 days of July 2018, the winds were insufficiently strong to break down the stratification, indicating that deeper waters of the bay are not well mixed. Particle tracking was used to illustrate how Hay Bay provides a habitat for algae growth within the bay.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".