Automatic quantification of escherichia coli bacteria to determine the potability of water
Bibliographic record
Abstract
Escherichia coli (E. coli) bacteria is one of the major biological contaminant of water in developing countries. According to WHO nearly 4 billion cases of diarrhoea and 2.2 million deaths in developing countries is because of E. coli. chlorination is an effective and scalable way of decontaminating and making water potable. The correct amount of chlorination is crucial; under-chlorination does not deactivate the E. coli completely and over-chlorination results in poor quality of water. Knowledge of the degree of contamination (measured in cfu) can be effectively used by local administration to decontaminate water by using exact amount of chlorine. Recently, Mobile Water Kit (MWK) has been proposed which can detect the presence of E. coli in water rapidly by manual inspection of pathogenic strains of E. coli. In this paper, we propose a robust and automatic method to quantify E. coli in water using image processing techniques. The choice of pre-processing techniques and identification of specific color based features makes the technique robust. Experimental results on 16 real images of water contaminated by E. coli taken by a mobile phone camera demonstrate the relevance of the features selected to identify the degree of contamination. The main contribution of this paper is the identification of an appropriate image color and structural features to estimate the degree of contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".