Race, Racism, and Drinking Water Contamination Risk From Oil and Gas Wells in Los Angeles County, 2020
Bibliographic record
Abstract
Objectives. To evaluate the potential for drinking water contamination in Los Angeles (LA) County, California, based on the proximity of supply wells to oil and gas wells, and characterize risk with respect to race/ethnicity and measures of structural racism. Methods. We identified at-risk community water systems (CWSs) as those with supply wells within 1 kilometer of an oil or gas well. We characterized sociodemographics of the populations served by each CWS by using the 2013–2017 American Community Survey. We estimated the degree of redlining in each CWS service area by using 1930s Home Owners’ Loan Corporation security maps, and characterized segregation by using the Index of Concentration at the Extremes. Multivariable regression models estimated associations between these variables and CWS contamination risk. Results. A quarter of LA County CWSs serving more than 7 million residents have supply wells within 1 kilometer of an oil or gas well. Higher percentages of Hispanic, Black, and Asian/Pacific Islander residents and a greater degree of redlining and residential segregation were associated with higher contamination risk. Conclusions. Redlining and segregation predict drinking water contamination risks from oil development in LA County, with people of color at greater risk. (Am J Public Health. 2023;113(11):1191–1200. https://doi.org/10.2105/AJPH.2023.307374 )
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".