Evaluation of Microbiological and Physicochemical Contamination of Groundwater in the Coastal Wetland of Pantanos de Villa (Lima‐Peru)
Bibliographic record
Abstract
(1) Background: Groundwater quality in tropical wetlands remains poorly studied. Los Pantanos de Villa, a key wetland located on Peru's desert coast, is recognized as a Protected Natural Area and a Ramsar site that faces ongoing threats. This study aimed to assess groundwater contamination in the wetland using microbiological (Escherichia coli, total and fecal coliforms) and physicochemical parameters, as well as water quality indices (WQIs); (2) Methods: In 2023 annual sampling was conducted at 12 locations (4 test pits and 8 piezometers), during the high recharge season (March) and low recharge season (September). Parameters were compared to national (Peru) and international (U.S.) standards. Two WQIs—the Canadian Council of the Ministry of the Environment (CCME) and the National Sanitation Foundation (NSF)—were applied; (3) Results: Microbiological contamination levels were dangerously high at all sampling sites. Physicochemical parameters exceeded permissible limits in at least one season. The CCME-WQI classified 100% of samples as having poor quality, and the NSF-WQI classified 75% as poor and 25% as medium quality; (4) Conclusions: Groundwater in the Pantanos de Villa wetland exceeds national and international contamination thresholds. Microbiological pollution is particularly concerning. WQIs confirm a degraded state.
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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 | 0.000 | 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 | 0.000 |
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".