A methodology for identifying vulnerable locations to taste and odour problems in a drinking water system
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
In this research, methodology was developed to identify locations vulnerable to taste and odour problems in a distribution system. The methodology was based on a multicriteria procedure combining spatial information on consumer complaints and popular perception of tap water. The first step in the described methodology consisted of mapping complaints regarding tap water made by the population between 2002 and 2004 using a geographical information system (GIS). The second step consisted of analysing results of a questionnaire-based mail survey, also through GIS. The information generated using the above steps was integrated using a multicriteria and spatial approach allowing segregation of the distribution system into delineated zones, according to their vulnerability to occurrences of taste and odour problems. The identification of vulnerable sectors in a distribution system will help water managers to implement a better-targeted water quality monitoring programme--one that considers odours and tastes of drinking water--within the management process.
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 | 0.007 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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