Transport models for the coastal pollution problems in the Great Lakes
Bibliographic record
Abstract
This paper utilizes a combination of physical limnological data and two types of transport models for predicting the fate and transport of material in Lake Ontario. In the first application a near-field model combined with a lake model is used to predict the waste plume characteristics for a proposed outfall in Lake Ontario. The outfall model results show that for treated effluents the near-field dilution ratios are satisfactory for the present discharge conditions. Far-field simulations showed no contamination near the existing Hamilton and Burlington water intakes. In another example, the transport and compartmental distribution of chlorinated benzenes in the Niagara River bar area were simulated using a two-dimensional model that combines coastal physical processes with a chemical partitioning sub-model. The Niagara River plume model demonstrated its applicability in the nearshore for short-term prediction of fate and transport of toxic chemicals in the coastal zone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".