Evaluation of Field Testing Techniques Used in a Household Water Treatment Study in Posoltega, Nicaragua
Bibliographic record
Abstract
Abstract A field evaluation of biosand filters, a method of household water treatment, was conducted in Posoltega, Nicaragua. The evaluation consisted of water quality testing for Escherichia coli and total coliforms using membrane filtration (MF) with m-coliBlue24 growth media, and SolarCult dipslides. For each sample tested, the MF procedure was applied to two volumes differing by an order of magnitude to capture the expected range of microbial contamination. A follow-up laboratory quality assurance/quality control procedure was undertaken to validate the field methods. The average coefficient of variation was 51%, with no statistically significant difference between the field and laboratory data. The MF procedure used with m-coliBlue24 produced useful reproducible results for the microbial concentrations encountered in the field. Dipslide results were found to correlate poorly to the membrane filtration results from the field, and should not be used to test drinking water due to the high limit of detection. They may still be useful for preliminary testing of highly contaminated source water as part of a household water treatment project. Dipslides may also be a useful tool for local health representatives to promote safe water practices within the community since they are simple to use and provide a visual indicator of water quality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".