Management Transition to the Great Lakes Nearshore: Insights from Hydrodynamic Modeling
Bibliographic record
Abstract
The emerging shift in Great Lakes management from offshore to nearshore waters will require attention to complexities of coastal hydrodynamics and biogeochemical transformations. Emphasizing hydrodynamics, this work resolves transport processes in quantifying discharge plume and pollutant of concern (POC) footprint dimensions, the latter being the portion of the plume where water quality standards are not met. A generic approach, isolated from pollutant-specific biokinetics, provides first-approximation estimates of the footprint area. A high-resolution, linked hydrodynamic-tracer model is applied at a site in the Greater Toronto Area on Lake Ontario. Model results agree with observed meteorological and hydrodynamic conditions and satisfactorily simulate plume dimensions. Footprints are examined in the context of guidelines for regulatory mixing zone size and attendant loss of beneficial use. We demonstrate that the ratio of the water quality standard to the POC concentration at discharge is a key determinant of footprint dimensions. Footprint size for traditional pollutants (ammonia, total phosphorus) meets regulatory guidelines; however, that for soluble reactive phosphorus, a presently unattended pollutant, is ~1–2 orders of magnitude larger. This suggests that it may be necessary to upgrade treatment technologies to maintain consistency with regulatory guidelines and mitigate manifestations of the eutrophication-related soluble reactive phosphorus POC.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".