Using Decision Trees to Predict Drinking Water Advisories in Small Water Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As of Jan. 1, 2015, there were 1,838 drinking water advisories (DWAs) in effect across Canada, including DWAs in First Nations communities. This research investigates the use of data‐mining techniques to identify which factors can potentially lead to a DWA in small water systems such as those found in First Nations communities in Canada. The results show that the training level of operators, remoteness/geographic location, source water type, and the class of treatment system are factors that influence whether a DWA is issued in a water system. The decision trees discussed in this study demonstrate that data mining is capable of correctly predicting up to 79% of future DWAs. This study demonstrates that a decisiontree methodology is a powerful, user‐friendly tool that can help water managers and regulators better understand vulnerabilities related to the provision of drinking water in small systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it