Proximity to Combined Sewer Overflow-Impacted Waters in Philadelphia: A Geographic Information Systems Study to Explore Environmental Injustice
Bibliographic record
Abstract
Background: Recreating in waterbodies impacted by combined sewer overflows (CSOs) can present health risks due to exposure to microbial pathogens. This study compares population characteristics of those living within walking distance to CSO-impacted versus nonimpacted waters in Philadelphia to determine whether these populations differ by race, ethnicity, and sociodemographic characteristics. Methods: Adults recreating at or near natural water bodies in Philadelphia completed a questionnaire that assessed the average walking distance to each site. Walking distance boundaries informed by questionnaire responses were created around each waterbody in Philadelphia, and population-level census data corresponding with block groups included within each buffer were used to characterize those living near a CSO-impacted and nonimpacted waterbodies in Philadelphia. Results: Compared with populations residing in census block groups within walking distance to a nonimpacted waterway, populations living within the same distance to a CSO-impacted waterway were more likely to comprise Hispanic residents (standardized adjusted prevalence ratio [APR] = 1.13) and those living in poverty (APR = 1.21) and less likely to comprise White residents (APR = 0.76). Conclusion: These findings suggest that communities of color and those experiencing poverty are disproportionally impacted by environmental hazards.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".