Prioritization of sites for sediment remedial action at Randle Reef, Hamilton Harbour
Bibliographic record
Abstract
Hamilton Harbour, an enclosed embayment at the western end of Lake Ontario, was designated as an Area of Concern in 1985 under the Great Lakes Water Quality Initiative. The harbour has been disturbed by wastes from steel industries, municipal wastewater discharges, and urban runoff. The area of the harbour known as Randle Reef has the largest volume of polycyclic aromatic hydrocarbon contaminated sediment on the Great Lakes and will be undergoing a major remediation project. An engineered containment facility will be constructed on top of the most contaminated sediment and surrounding sediments will be dredged and placed inside the engineered containment facility, or capped. To determine where remediation was required, areas were prioritized according to toxicity and contaminant concentrations. Although benthic invertebrate community assessment can be the best indicator of in situ toxicity, this was problematic in the harbour due to lack of appropriate reference areas for comparison and potential effects of other stressors (e.g. disturbances of sediments from ship traffic, seasonal low hypolimnetic oxygen concentrations). Initial prioritization of areas for remediation was therefore based on toxicity to benthic invertebrates and the associated contaminant levels. In 2002, 80 sites were sampled and laboratory bioassays conducted with the Amphipod Hyalella azteca and the Mayfly Hexagenia spp. Initial site prioritizations were established based on: (1) high contamination and toxicity; (2) high contamination and no/low toxicity; (3) low contamination and toxicity; and (4) low contamination and no toxicity. Final prioritizations considered study design and area considerations as well as additional sediment data obtained to address data gaps. Of the 80 sites, 79% were classed in a priority subgroup, while remaining sites were deemed non-priority.
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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 | 0.001 | 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) | 0.001 | 0.001 |
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".