Persistent Organic Pollutants (Pops) Contamination In The Gulf Of St Lawrence: A Critical Environmental Health Issue For The Population Of Newfoundland (Canada)
Bibliographic record
Abstract
Introduction: The Great Lakes and the St Lawrence River are heavily polluted with many different kinds of Persistent Organic Pollutants (POPs) from industrial activities, agriculture and numerous landfills in the US and Canada. Many species native to the river and the estuary show evidence of POPs contamination, indicating that these chemicals have moved up the food chain to higher trophic levels. The local population inhabiting the coasts of the river and estuary in Quebec are known to have serious health consequences due to consumption of contaminated local seafoods. Since the Gulf of St Lawrence is the primary drainage for the river, it can be expected that the local marine species are also contaminated with POPs. We intended to examine the extent of contamination of marine species in the Gulf of St Lawrence and estimate the health risks to the population of the adjacent island of Newfoundland. Methods: Peer-reviewed journal articles were retrieved using Google Scholar, PubMed and Science Direct. We used 'Gulf of St. Lawrence' and 'Newfoundland' as the standard terms and added one of the pollutants (persistent organic pollutants, pesticides, organochlorine, organophosphate, dioxin, dichlorodiphenyltrichloroethane, polychlorinated biphenyls, and polybrominated diphenyl ethers in each search. Results: There were 418 journal articles that contained our search terms. However there was no publication of a population-based survey of the adverse health impacts of POPs in Newfoundland. Discussion: The total population of the island of Newfoundland is over half-a-million, the majority of which live along the coast and regularly consume inshore seafood as part of local customs. Despite scattered information on pollution of marine species of the Gulf of St. Lawrence, there is virtually no report of health risks due to consumption of contaminated seafood products from the Gulf. We suggest conducting an extensive survey of exposure assessment and health risk of the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".