Social vulnerability and exposure to private well water
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
One quarter of the population of Pennsylvania relies on private domestic well water: two-fold greater than the US average. Private well owners are responsible for the maintenance and treatment of their water supply. Targeted interventions are needed to support these well owners to ensure they have access to safe drinking water, free of contaminants. To develop appropriate interventions, an understanding of the characteristics and social vulnerability of communities with high well water use is needed. The purpose of this study was to determine the spatial patterning of social vulnerability in Pennsylvania and assess the association between social vulnerability and private domestic wells using profile regression. Census data and water supply information were used to estimate the proportion of the population using domestic wells. Ten area-level measures of social vulnerability at the census-tract level were investigated, using Bayesian profile regression to link clustering of social vulnerability profiles with prevalence of private domestic wells. Profile regression results indicated 15 distinct profiles of social vulnerability that differ significantly according to the area-level prevalence of domestic well use frequency. Out of these, two profiles of census tracts were identified as socially vulnerable and had a high proportion of well-water users, representing approximately 1.1 million Pennsylvanians or a third of all well water users in the State. High area-level social vulnerability profiles coincide with a high frequency of private well-water use in PA. This study presents a data-driven approach to supporting public health programs aimed at reducing exposure and health risks of chemical and infectious agents in household water supplies by targeting vulnerable populations.
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 | 0.000 | 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) | 0.001 | 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