Comparison of methods to analyse imprecise faecal coliform count data from environmental samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imprecise values arise when bacterial colonies are too numerous to be counted or when no colonies grow at a specific dilution. Our objective was to show the usefulness of multiple imputation in analysing data containing imprecise values. We also indicate that interval censored regression, which is faster computationally in situations where it applies, can be used, providing similar estimates to imputation. We used bacteriological data from a large epidemiological study in daycare centres to illustrate this method and compared it to a standard method which uses single exact values for the imprecise data. The data consisted of numbers of FC on children's and educators' hands, from sandboxes and from playareas. In general, we found that multiple imputation and interval censored regression provided more conservative intervals than the standard method. The discrepancy in the results highlights both the importance of using a method that best captures the uncertainty in the data and how different conclusions might be drawn. This can be crucial for both researchers and those who are involved in formulating and regulating standards for bacteriological contamination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it