An automated and high‐throughput method for adenosine triphosphate quantification
Bibliographic record
Abstract
Abstract Exposure to microbial contamination through drinking water is a major global health concern. Effective management of microbial drinking water quality requires rapid detection equipment. Currently, microbial quality is monitored using time‐consuming laboratory methods, which delay any response. This study demonstrates the development of an automated and high‐throughput method for the measurement of viable biomass in water through the quantification of cellular adenosine triphosphate (ATP). The developed method was able to efficiently and accurately quantify cellular ATP in multiple water samples simultaneously. In addition, it proved to be 5× faster and as accurate as the Standard Test Method for Adenosine Triphosphate (ATP) Content of Microorganisms in Water (ASTM D4012). The developed method has the potential to represent a significant advancement for microbial monitoring and could benefit utilities interested in measuring viable biomass in water to monitor the health of biofilters and the effectiveness of disinfection strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".