Evaluation of the adenosine triphosphate (ATP) bioluminescence assay for monitoring effluent quality and disinfection performance
Bibliographic record
Abstract
This study investigated the use of the adenosine triphosphate (ATP) bioluminescence assay as a tool for monitoring water and wastewater quality and disinfection performance subsequent to ultraviolet (UV) irradiation and chlorine disinfection. Two different commercially available ATP assays were used in the study and controlled experiments were carried out using a pure Escherichia coli culture to determine how the ATP content of samples change after they are exposed to UV and chlorine. Finally, a selected assay was used with samples collected from drinking water and wastewater treatment plants to assess its potential use by treatment plants for process and effluent monitoring. The ATP assay could detect the chlorine damage to cells but the detection limit of the assay was not sensitive enough to determine the level of chlorine disinfection performance. No clear trend was observed between UV irradiation and ATP content of the cells. Samples were also collected from water and wastewater treatment plants and a good correlation was observed between the culture-based methods and the ATP assay results, which indicate the potential use of the ATP assay as a process and effluent quality monitoring tool at treatment plants.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".