Should the AOAC Use-Dilution Method Be Continued for Regulatory Purposes?
Bibliographic record
Abstract
Despite its very poor reproducibility, AOAC INTERNATIONAL's use-dilution method (UDM) for bactericidal activity (AOAC Methods 964.02, 955.14, and 955.15) has been required by the U.S. Environmental Protection Agency (EPA) since 1953 for regulatory purposes, while methods with better reproducibility have been adopted in Canada and Australia. This study reviews UDM from a statistical perspective. Additionally, the test's expected results were compared to those obtained from actual evaluation of several formulations. Significant gaps have been identified in the reproducibility of the test data as predicted by statistical analysis and those presented to the EPA for product registration. UDM's poor reproducibility, along with its qualitative nature, requires the concentration of the active ingredient to be high enough to ensure all or most carriers to be free of any viable organisms. This is not in accord with the current trends towards sustainability, human safety, and environmental protection. It is recommended that the use of the method for regulatory purposes be phased out as soon as possible, and methods with better design and reproducibility be adopted instead.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".