Innovative Treatment of Wood Waste Sediments Using Reactive Amendments and DGT Passive Porewater Sulphide Testing Techniques
Bibliographic record
Abstract
Esquimalt Harbour has historically been used for log rafting, log storage and wood mill operations over the last 70 years, resulting in the accumulation of over 200 hectares of wood waste deposits. As wood waste decomposes, it creates a biological oxygen demand in sediments that can reduce or eliminate oxygenated zones. This can lead to a buildup of compounds such as sulphides and ammonia, which are toxic to benthic organisms at higher concentrations. Public Services and Procurement Canada, on behalf of the Department of National Defence, has completed studies of wood waste sediments and is currently constructing a pilot project to address high sulphides in Esquimalt Harbour sediments. The studies include use of an innovative passive porewater sampling technique to quantify dissolved sulphide using the diffusive-gradient-in-thin-films (DGT) method to quickly and accurately measure porewater sulphide concentrations, which ranged from less than 1 mg/L to over 200 mg/L in harbour sediments. The DGT method is based on the reaction of sulphide with silver iodide and is becoming increasingly common as a reliable in situ technique for quantifying a range of sediment porewater constituents. Cleanup of wood waste impacted sediments has historically involved dredging, capping, or monitored natural recovery. However, in situ treatment amendments have the potential to oxidize or immobilize porewater sulphide. An innovative bench-scale testing program was conducted to assess the effectiveness of sand cover mixed with a range of treatment amendments to reduce bioavailable porewater sulphide concentrations in wood waste sediments. The results were used to design and construct a pilot project in Esquimalt Harbour to test the effectiveness of sand amended with iron carbonate to control sulphide concentrations and support a healthy benthic community. This presentation will describe the field investigations, bench scale testing, design and construction of the pilot project, and initial monitoring results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".