Tracking PCB Contamination in Ontario Great Lakes Tributaries: Development of Methodologies and Lessons Learned for Watershed Based Investigations
Bibliographic record
Abstract
Project Trackdown is an investigative environmental program aimed at tracking sources of polychlorinated biphenyl (PCB) contamination in Great Lakes tributaries. The program uses a multimedia weight of evidence approach for identifying sources of PCBs to the environment. PCB concentrations in environmental media (sediment, water, suspended sediment and soil), passive samplers and/or exposed biota (mussels, young-of-the-year fish and benthic invertebrates) are used in combination to evaluate bioavailability and identify local anomalies within a tributary. These lines of evidence can be assessed with simple chemometric techniques and fingerprinting of PCB congener profiles, and, combined with anecdotal information such as land use history and tributary alterations, may be used to identify ongoing and locally controllable sources of PCBs to the Great Lakes. The program was successful at developing environmental triggers to differentiate potential source areas from background PCB conditions in urban areas, allowing efforts to focus on identifying active ongoing sources of PCB contamination. Project Trackdown has been carried out in three tributaries to Lake Ontario (Cataraqui River, Etobicoke Creek and Twelve Mile Creek) and two tributaries that flow into the Detroit River (Turkey Creek and Little River). Local ongoing PCB sources have been identified in four projects, leading to abatement or remediation measures. As a collaborative initiative between the Ontario Ministry of the Environment and Environment Canada, Project Trackdown has successfully identified several PCB sources leading to substantial cleanup efforts aimed ultimately at reducing PCB contamination to the Great Lakes.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".