Factoring Stormwater Source Control and Natural Recovery Into Long-Term Risk Management Planning for Harbour Sediment
Bibliographic record
Abstract
Sediments within the Lot 17 area of Victoria Harbour (B.C.) are managed by Transport Canada. Over the past several years, Transport Canada and Public Works Government Services Canada (PWGSC) have been evaluating the nature and extent of contaminated sediments within the Lot 17. These studies have included the recent completion of a human health and ecological risk assessment. These studies have also included evaluation of ongoing source inputs and natural recovery processes to understand the role of these processes in controlling contaminant concentration trends over time in sediments and harbour biota.Ongoing stormwater source inputs were evaluated, and these were found to be significant particularly for polychlorinated biphenyls (PCBs). The most significant inputs were associated with a small number of stormwater outfalls located in current/former industrial areas. Contaminant loadings from stormwater outfalls were modeled to evaluate the significance of these inputs both to the aquatic food web, as well as to potential recontamination of harbour sediments. These studies have been useful to prioritize certain stormwater basins for coordinated source control work with the local municipality, in parallel with risk management planning for harbour sediments.Natural recovery processes within the harbour have been quantified directly using sediment geochronology cores, and using trend analysis in sediments and tissues over time. Despite the presence of ongoing source inputs, natural recovery rates have been shown to be significant both for harbour sediments. The decreasing contaminant trends observed for sediment are also observed in harbour biota. Substantial reductions in PCB and dioxin/furan concentrations have been documented in the tissue of Dungeness Crab during over 20 years of biomonitoring. Evaluation of risk management options for the harbour sediments has considered ongoing monitored natural recovery and its benefits to further long-term risk reduction.Ongoing remediation and risk management planning is focused on detailed options evaluationfor a localized hot spot area within the harbour. For other harbour areas with low defined risks,risk management planning will include coordination of source control activities with the localmunicipality, and monitoring of recovery in sediment and tissue contaminant concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".