A Simple Off-Grid Incubator for Microbiological Water Quality Analysis
Bibliographic record
Abstract
There is a need for accessible and low-cost microbiological water quality testing in contexts where diarrheal illness is a major public health concern. In most cases, the quantification of Escherichia coli and other microbial indicators by conventional culture methods requires an incubation step for processed samples at specific temperatures for bacterial growth over a prescribed time. However, incubators can be the most expensive equipment required for such microbial analyses, limiting the number and scope of water quality testing available in low-resource contexts. In this study, a low-cost incubator was developed using a locally available expanded polystyrene (EPS) foam cooler, with two water bottles filled with hot water to heat incubator to a target of 35 °C. The EPS incubator performance was validated by processing 150 water samples in duplicates using the Colilert Quanti-tray/2000 system, incubated in either the EPS incubator or a standard laboratory incubator set at 35 °C. Statistically significant correlations of results indicated that the quantification of E. coli was comparable between both methods. Risk categorizations from standard and EPS incubation results agreed for 141 of 150 (94%) samples, with zero false negatives. In addition to being reasonably mobile the EPS incubator would reduce the cost of such water quality testing, thus potentially increasing the scope of water quality testing coverage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".