On-site Sanitation Influence on Nitrate Occurrence in the Shallow Groundwater of Mahitsy City, Analamanga Region, Madagascar
Bibliographic record
Abstract
Nitrate contamination of groundwater has inclined to be a critical issue in areas where groundwater is the only available resource for water supply for drinking use purpose. In developing countries such as Madagascar, on-site sanitation can be a significant source of nitrate contamination of shallow groundwater, depending on the type of sub-surface layer and hydrogeological environment, the arrangements and behavior of sanitation, and the design of sanitation used for defecation. This study was carried out to investigate the nitrate occurrence in shallow groundwater of Mahitsy city, Analamanga Region of Madagascar, and to assess the on-site sanitation influence on nitrate concentration in drinking water well. Water samples were collected from dug wells in rainy and dry seasons. The analytical results showed that the measured nitrate concentration was in the range of 1.5 mg/L and 580 mg/L with an average of 348 mg/L for all water samples. Thirteen out of fifteen samples had nitrate concentration exceeding the WHO guideline value (50mg/L). Data analysis indicated that nitrate concentration in dry season (average 409 mg/L) was greater as compared to rainy season (371 mg/L). However, the difference was not significant at the 0.05 level. Significant positive correlation (0.849, p < 0.01) was found between nitrate and chloride concentration with chloride/nitrogen ratio of about 1:2.23, suggesting the same source for nitrate and chloride. Nitrate concentrations of well waters were strongly correlated to distance between water wells and sanitation facilities (-0.466, p = 0.08), to water table level (-0.558, p < 0.05) and to age of water wells (0.655, p < 0.01).
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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".