Impact of the Monthly Variability of the Trent River on the Hydrodynamical Conditions of the Bay of Quinte, Ontario: A Case Study 2016–2019
Bibliographic record
Abstract
The spatial and temporal (monthly) variability of river discharge has a significant effect on circulation and transport pathways within shallow embayments whose dynamics are largely controlled by wind and riverine inputs. This study illustrates the effects of the monthly variation in Trent River discharge on simulated particle transport and settling destination in the Bay of Quinte, Lake Ontario for the years 2016–2019. Observations of Lagrangian surface drifter data were used to derive Trent River discharge forcing for a three-dimensional hydrodynamic numerical model of the Bay of Quinte. Peak monthly flushing was up to three times as much as the lowest monthly flushing in any year, with the Trent River responsible for up to 95% of the flushing in low runoff years. Particle transport simulations showed that particles could be trapped along shorelines, which extended residence times, and Trent River releases suggest that researchers should look for delayed peaks in Total Phosphorous (TP) load measurements in observations between Trenton and Belleville as particles move downstream. Particles with constant settling velocities originating from the Trent River did not move downstream past Big Bay, and particles from the Napanee River were the primary source for Longreach.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".