Key Factors Influencing Drinking Water Advisories on Indigenous Reserves in Canada: An XGBoost Analysis
Bibliographic record
Abstract
Access to safe drinking water on Indigenous reserves is a serious issue within Canada. This research identifies the importance of variables in determining the duration and frequency of drinking water advisories (DWAs). Data related to Indigenous communities were collected from a variety of federal agencies and combined into a single dataset. XGBoost, a machine learning algorithm, was used to characterize the importance over 19 years of available DWA data from 2004 to 2023. The results show the importance of factors such as types of reservoir and operator certification level for long-lasting and frequent DWAs. Underground and surface reservoirs are shown to be susceptible to microbial contaminants, and the small size of some reservoirs can lead to insufficient chlorine contact time. The operator’s status is significant in determining duration, as a community with no certified operator is 3.8 times more likely to have a DWA that lasts more than two weeks, compared to a level IV operator. These findings can potentially inform decision-makers as to which communities require more assistance and effective strategies for allocating financial resources. This research highlights the importance of ensuring modern infrastructure is provided for Indigenous communities in Canada and financial resources are allocated to hire qualified individuals to operate the infrastructure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".