Understanding the Vegetable Contamination Process with Parasites from Wastewater Irrigation and Its Impact on Human Health in Hidalgo, Mexico
Bibliographic record
Abstract
The use of untreated municipal wastewaters for irrigating agricultural crops negatively affects human health. Thus, the sewage effluent from the city of Pachuca, in the state of Hidalgo, Mexico, used for agricultural purposes was the most important reason to undertake this research work, whose main objective was to understand the process that involves its current use as irrigation water, and the potential harm to human health because the raw vegetables produced using this irrigation scheme are being consumed by the general public. The content of fecal coliforms and helminth eggs in wastewaters were determined and the level of parasitological contamination of vegetables and the potential number of people affected was estimated due to the consumption of raw produce without proper pretreatment, a common practice in Mexico. The potential level of parasitological contamination of vegetables was estimated by analyzing bibliographic data collected under similar climatic and technological conditions as in Pachuca. Results indicate that the level of wastewater contamination from fecal coliforms in Pachuca was 5000 times higher than the maximum permissible level based on Mexican standards for irrigation waters, and for Ascaris lumbricoides L. and Hymenolepis diminuta (Rudolphi) eggs up to 64 times. The number of persons potentially infected through consumption of raw vegetables irrigated with this contaminated water was estimated to be 169,000 annually.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".