Assessment of monitoring approaches to control Legionella pneumophila within a complex cooling tower system
Bibliographic record
Abstract
Precise and rapid methods are needed to improve monitoring approaches of L. pneumophila (Lp) in cooling towers (CTs) to allow timely operational adjustments and prevent outbreaks. The performance of liquid culture (ASTM D8429-21) and an online qPCR device were first compared to conventional filter plate culture (ISO 11731-2017), qPCR and semi-automated qPCR at three spiked concentrations of Lp (serogroup 1) validated by flow cytometry (total/viable cell count). The most accurate was qPCR, followed by liquid culture, online and semi-automated qPCR, and lastly, by a significant margin, filter plate culture. An industrial CT system was monitored using liquid and direct plate culture by the facility, qPCR and online qPCR. Direct plate and liquid culture results agreed at regulatory sampling point, supporting the use of the faster liquid culture for monitoring culturable Lp. During initial operation, qPCR and online qPCR results were within one log of culture at the primary pump before deviating after first cleaning. Other points revealed high spatial variability of Lp. The secondary pumps and chiller had the most positivity and highest concentrations by both qPCR and liquid culture compared to the basin and infeed tank. Altogether, this suggests that results from monthly compliance sampling at a single location with plate culture are not representative of Lp risks in this CT due to the high temporal and spatial variability. The primary pump, rather than the CT basin, should be designated for sampling, as it is representative of the health risk. An annual multi point survey of the system should be conducted to identify and target Lp hot spots. Generally, a combination of liquid culture for compliance and frequent qPCR for process control provides a more agile and robust monitoring scheme than plate culture alone, enabling early treatment adjustments, due to lower limit of detection (LOD) and turnover time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".