Examining Priority Contaminants and Farmer Interventions in Private Water Wells: A Scoping Review
Bibliographic record
Abstract
Groundwater from private wells is a vital resource for agricultural productivity, rural livelihoods, and drinking water in North America. However, contaminants such as nitrates, pesticides, heavy metals, pathogens, and emerging pollutants like per- and poly-fluoroalkyl substances (PFAS) pose significant threats to water quality and public health. This research aims to examine the prevalence of these contaminants, evaluate farmer-led interventions, and identify knowledge gaps to inform sustainable water management practices. Using a scoping review methodology, guided by the PRISMA-ScR framework, the project systematically reviews literature published in the last decade. The study focuses on agricultural regions in North America, where private well reliance is high, and contamination risks are prevalent. It evaluates interventions such as maintenance, filtration systems, and runoff management while identifying barriers to implementation, particularly in resource-constrained rural communities. Although still preliminary, this project has identified significant research gaps, including the long-term health effects of emerging contaminants and the accessibility of mitigation technologies for rural users. Initial findings emphasize the importance of proactive policies, community engagement, and innovative technologies to safeguard groundwater resources. This ongoing work will advance knowledge on the intersection of agriculture and water quality management, promoting actionable solutions to enhance rural resilience and sustainable groundwater use. The symposium presentation will highlight the study's objectives, methodology, and initial findings while outlining future directions for this critical area of research."
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.
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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".