OPMS - A web-based ocean pollution monitoring system
Bibliographic record
Abstract
Marine pollution poses significant risks to both marine ecosystems and human health, requiring effective monitoring and control measures. This study presents the Ocean Pollution Monitoring System (OPMS), a web application designed to visualize the seasonal and annual fluctuations of marine pollutants along coastal regions in Canada. The pollutants include fecal coliform and biotoxins such as paralytic shellfish poisoning (PSP), and amnesic shellfish poisoning (ASP). The OPMS utilizes 20 years of data from nearly 15,000 shellfish harvesting sites across six provinces of Canada, allowing users to explore trends and the impact of these pollutants in user-selected geographical regions. The seasonal fluctuation patterns of fecal coliform and biotoxin levels were extracted by Functional Principal Component Analysis (FPCA) previously. OPMS visualizes these results in finer granularity to provide environmental managers and policymakers with a decision-support tool in shellfish safety and water quality management. The tool is accessible at http://opms.uvic.ca . • A web-based, interactive application was built to monitor fecal coliform and biotoxins. • Data was amassed from 15,000 sites across 6 coastal Canadian provinces over 20 years. • The results are displayed through the FPC score, reflecting in the level of contamination at each site. • Variability in the pollutant levels was evident in overall amplitude and seasonal changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".