COMPARAÇÃO DA QUALIDADE DA ÁGUA USANDO MÉTODOS MULTIVARIADOS E ESPACIAIS EM UMA REDE DE DISTRIBUIÇÃO DE ÁGUA
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
This study analyzed interventions in a drinking water supply system from 2016 to 2021 over a six-year period using statistical tools and a Geographic Information System (GIS) for detailed mapping. This research highlighted correlations between turbidity, apparent color, and iron content, which significantly influenced water quality. Key interventions included increasing the chlorine dosage and replacing old metal pipes with new plastic ones, resulting in an overall 6% improvement in water quality. Although the Modified Water Quality Index by the Canadian Council of Ministers of the Environment identified certain districts with critical water quality levels, only spatial analysis could pinpoint specific pipes and areas within these districts that showed improvements, thereby guiding future investment strategies.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it