Pollution Tracker: Long-term Monitoring of Priority Contaminants in Coastal British Columbia
Bibliographic record
Abstract
Pollution Tracker is the first long-term marine pollution monitoring program in Canada (pollutiontracker.org). Established in 2015, the program currently operates coast-wide in British Columbia (BC) with over 60 sampling sites established to date. Collaboration with coastal First Nations, government agencies, port authorities, industry, and community groups has enabled the completion of Phase 1 and 2 and the implementation of Phase 3. Mussels and nearshore subtidal sediment are being used to monitor spatial and temporal trends of both legacy and emerging contaminants of concern. Over 450 individual analytes from 14 contaminant classes are being measured using high-resolution analysis. Both current-use and legacy chemicals and microplastics are being detected, including those identified as priority contaminants of concern for southern resident killer whales (SRKW) and their prey. Despite documented declines of legacy contaminants in the Salish Sea since regulations were implemented (i.e., PCBs, PBDEs), they continue to pose risks to marine organisms, and inputs of currently used, less well understood contaminants are increasing. Long-term monitoring of these contaminants at the base of the food chain will provide an overview of the state of the marine environment, inform on distribution and persistence, and provide data that can be used to assess potential adverse health effects and influence policy decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".