Comparison of <i>Legionella pneumophila</i> and <i>Pseudomonas fluorescens</i> Quantification Methods for Assessing UV LED Disinfection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study assesses the efficacy of ultraviolet light-emitting diodes (UV LEDs) for deactivating Legionella pneumophila (pure culture) and Pseudomonas fluorescens (pure culture and biofilms) on relevant drinking water distribution system surfaces (cast iron and stainless steel). UV LED treatment at 280 nm demonstrated superior performance compared to that at 365 nm, achieving a 4.8 log reduction value (LRV) for P. fluorescens pure cultures and, for biofilms, 4.02 LRV for stainless steel and 2.96 LRV for cast iron at 280 nm. Conversely, the results were less effective at 365 nm, with suspected photolytic reactions on cast iron. Quantification of L. pneumophila yielded varying results: 4 LRV using standard plate counts, 1.8 LRV with Legiolert, and 1 LRV with quantitative polymerase chain reaction at 280 nm, while the results were less than 1.5 LRV at 365 nm. This study provides insights into managing opportunistic pathogens and biofilms, emphasizing the need for improved quantification tools to better assess treatment efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".