Quantitative framework for validating two methodologies that are used to enumerate viable organisms for type approval of ballast water management systems
Bibliographic record
Abstract
≥ 10 μm to <50 μm in discharge water for administrative type approval of a ballast water management system (BWMS). Only MPN is suitable for assessing the efficacy of disinfection using UV radiation - a chemical-free treatment technology - but the U.S. Coast Guard has not approved an MPN-based approach as an alternate to their required S-M method. Approval depends on a demonstration of equivalence, but a framework for comparative validation is not well established. The purpose of this study is to provide such a framework. It is shown that the requirement for 5 consecutive successful results in BWMS type approval testing fundamentally changes the relationship between a method's precision and its effectiveness in ensuring compliance with regulations. False approval due to random measurement error is effectively eliminated for both methods because it requires 5 consecutive underestimates, and false rejection due to a single erroneously high measurement is more likely for the method with wider confidence limits, imposing an extra margin of safety for MPN. These results reverse conventional interpretations of efficacy based on method precision alone. Sources of systematic error (bias) are reviewed and methods for estimating the errors are described. If combined bias is positive (overestimation), either method would yield type-approval results fully compliant with regulations. Subject to similar negative bias, the less precise method (generally, MPN) would be more protective of the environment. An illustrative analysis of 64 paired MPN and S-M counts from BWMS trials indicates that neither method was significantly biased relative to the other. Considered in the framework for method validation described here, available evidence strengthens arguments that in a BWMS type approval testing regime, the efficacy of the MPN method is equivalent to that of Stain-Motility.
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.
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 | 0.208 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".