Quantification of uncertainty in microbial data—reporting and regulatory implications
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
Microbial contaminants are often regulated differently than chemical contaminants. Microorganisms are enumerated by techniques that are frequently susceptible to considerable losses, resulting in highly variable recoveries. Accordingly, several treatment technique‐based regulations have evolved for microbial treatment. However, even these regulations ultimately require some reliance on microbial concentration data. Statistical approaches have been developed for the calculation of confidence intervals for microbial concentrations and removals by treatment processes, and these approaches take into account the various errors associated with microbial enumeration. The approaches were used here to demonstrate the relationship between methodological error and the practicality of concentration‐based regulations that require continuous and/or frequent monitoring, demonstrate the necessity of treatment technique‐based regulations such as the Long Term 2 Enhanced Surface Water Treatment Rule, demonstrate that methodological uncertainty is more substantially reduced by increasing organism count than by improving methodological recovery, and propose sampling targets of approximately 10 or more organisms to appreciably reduce the uncertainty associated with microbial quantification
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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