A modeling assessment of contaminant fate in the Bay of Quinte, Lake Ontario: Part 1. Metals
Bibliographic record
Abstract
A mass balance model of contaminant fate-transport was used to assess the fate of four metals: As, Cd, Cu and Zn, in the Bay of Quinte for hydrologic conditions and loadings in 2000. Results were compared with previous model results of 1988. The model was based on the QWASI (Quantitative Water Air Sediment Interaction) approach and the fugacity/aquivalence concept. The Bay was divided into five geographic segments based on hydrodynamics and chemical loadings. The model identified tributaries and Lake Ontario as the major sources of metal loadings to the Upper and Lower Bays, respectively. Metal concentrations in water decreased by 40–75% in the tributaries between May 1988 and 2000, which resulted in decreased metal concentrations that were, in 2000, all below the Provincial Water Quality Objectives. Measured sediment concentrations exceeded the Lowest Effect Levels (LEL) for all metals at many sites in the Upper and Lower Bays. Using 2000 metal loadings from tributaries and Lake Ontario, the model predicted that sediment concentrations will meet or come within 20% of LELs for all metals in segment 1, As in segments 2 and 3, and Zn in segment 3 within 40 years. Although the model predicted that sediment concentrations would decline to the LEL for all segments within 26 (Zn) to 54 (Cu) years, evidence suggests that benthos are now not impaired by ambient sediment concentrations (excluding “hot spots”). Reducing sediment concentrations faster is expected to be very difficult because their loadings originate from diffuse sources in the watersheds of tributaries and Lake Ontario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".