A GIS-Based Model to Assess the Risk of On-Site Wastewater Systems Impacting Groundwater and Surface Water Resources
Bibliographic record
Abstract
On-site systems are successfully used across Ontario and Canada in rural areas to treat and disperse wastewater in areas without access to centralized sewer facilities. Ontario has detailed technical guidelines for the design and installation of on-site systems to help safeguard against contamination to public health and the environment. However, since on-site systems are managed by private users, there are significant risks to either surface and groundwater resources when the systems malfunction or are improperly operated and maintained. This research assesses and models these risks on a regional basis utilizing a GIS-based assessment tool. The developed risk assessment model uses nine pertinent parameters to account for contaminant loading and pathways, and system characteristics. Risk parameters included soil type, slope, lot size, surface water proximity, floodplain, groundwater intrinsic susceptibility, recharge areas and water supply proximity. When applied to Huron-Kinloss Township in Ontario, at-risk areas were successfully determined by the model and then confirmed and validated by local experts. Soil type, groundwater intrinsic susceptibility and system age were the greatest contributors to the overall risk for this area. The GIS-based model is useful for decision-makers in identifying at-risk areas for targeted management strategies such as prioritized re-inspection programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".