Groundwater research in southern Ontario by Environment and Climate Change Canada
Bibliographic record
Abstract
Over the past decade, the groundwater-specific research of Environment and Climate Change Canada has focused on groundwater transport of pollutants to surface waters. Within southern Ontario, the key pollutant of interest has been phosphorus, due to its link to eutrophication and harmful algal blooms of the Great Lakes and its watershed. Our work has targeted point sources, such as domestic wastewater septic systems, as well as broader-scale inputs within both urban and rural landscapes. Other contaminants have also received interest, including chloride (road salt), metals, and both legacy and emerging organic contaminants (e.g., pharmaceuticals, per- and polyfluoralkyl substances (PFAS), organophosphate flame retardants, etc.), especially in urban environments. A new study is looking at a range of these chemicals in old, closed landfills, of which thousands reside across southern Ontario. These substances pose a toxicity risk to aquatic ecosystems in the receiving environment and may spread more broadly from there. This work has largely employed two methodological "tools", i) surface water receptor-targeted groundwater sampling, and ii) the analysis of artificial sweeteners. The former allows for rapid acquisition of a large number of samples at relatively moderate expense in comparison to the use of wells. It also provides more exact information on the concentrations or mass of contaminants impacting the receptor. Artificial sweeteners can serve as a tracer of wastewater and landfill leachate, thus being a proxy for the possible presence of other wastewater or leachate contaminants and allow some quantification of their potential inputs. Their presence can also guide targeted sampling of more costly analytes. Here we provide a brief overview of our recent research in southern Ontario with some key examples of how these two tools have provided important results on the risks posed by groundwater-sourced pollutants to Great Lakes ecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".