Environmental Effects Monitoring in Sydney Harbor During Remediation of One of Canada's Most Polluted Sites: A Review and Lessons Learned
Bibliographic record
Abstract
This article reviews a comprehensive marine environmental effects monitoring program (MEEMP) comprised of components capable of detecting changes in the marine environment over short or extended temporal scales during remediation of one of Canada's most polluted sites at the Sydney Tar Ponds. The monitoring components included: water and sediment quality, amphipod toxicity testing, mussel tissue, crab hepatopancreas tissue, and benthic community assessments. The MEEMP was designed to verify the impact predictions for the remediation project (i.e., no immediate damage to the marine ecosystem through remediation activities). Some components were capable of providing conclusive data (e.g., sediment and water quality), while others only yielded data that were inconclusive or difficult to attribute to remediation activities (e.g., intertidal community assessments and amphipod toxicity testing). Components that provided only inconclusive results or were difficult to attribute to remediation activities were discontinued, resulting in substantial cost savings during the project, but without compromising the overall objectives of the program, which was to monitor for potential adverse environmental effects of remediation on the marine environment in Sydney Harbor and to verify environmental effects predictions made in the Environmental Impact Statement for the project. The rationale for discontinuing certain MEEMP components and discussion of conclusive results are incorporated into “lessons learned” for environmental remediation practitioners and regulators working on similar large‐scale multiyear remediation projects. © 2014 Wiley Periodicals, Inc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".