Concentrations and loads of metals, nutrients and organic contaminants entering the St. Lawrence River at Wolfe Island, 2000 to 2019
Bibliographic record
Abstract
Water quality trends and loads were analyzed at Wolfe Island for the years 2000 to 2019. This station captures the nutrient and contaminant concentrations leaving the Canadian Great Lakes system into the St. Lawrence River. In addition to tracking what is leaving the Great lakes system, this station provides an indication of contaminants flowing downstream where a number of sensitive areas exist such as the Thousand Island National Park as well as the St. Lawrence River Area of Concern at Cornwall. In terms of trends, trace metals and PAHs are generally decreasing at Wolfe Island while the nutrients and major ions are increasing. Organic compounds are more challenging to summarize since the number of non-detects prevented modeling of many or the frequency of analysis was too low to model. In a general sense, there is an overall decreasing trend in the organics and the large number of compounds whose concentrations are below detection levels does signify the very low concentration of these contaminants. A notable change in trend predominantly for the metals was noted around 2010 and is discussed herein. The amount of recent (5 years) exceedances of the most stringent water quality guidelines is lower than the previous study period (only PCBs and phosphorus, PFOS and most likely dieldrin). While there are many additional downstream sources of contaminants after the Wolfe Island station, the reductions observed from this study indicate a lower contribution from the Great Lakes in many cases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".