<i>Legionella</i>and non-<i>Legionella</i>bacteria in a biological treatment plant
Bibliographic record
Abstract
Legionella pneumophila were previously identified in the aeration ponds (up to 10(10) CFU/L) of a biological wastewater treatment plant at Borregaard Ind. Ltd., Sarpsborg, Norway, and in air samples (up to 3300 CFU/m(3)) collected above the aeration ponds. After 3 outbreaks of Legionnaires' disease reported in this area in 2005 and 2008, the aeration ponds of the plant were shut down by the Norwegian authorities in September 2008. The aim of the present work was to analyze the Legionella and non-Legionella bacterial communities in the aeration ponds before and during the shutdown process and to identify potential human pathogens. The non-Legionella bacterial community was investigated in selected samples during the shutdown process by 16S rDNA sequencing of clone libraries (400 clones) and growth analysis. The concentration of L. pneumophila and Pseudomonas spp. DNA were monitored by quantitative PCR. Results showed a decrease in the concentration of L. pneumophila and Pseudomonas spp. during the shutdown. This was accompanied by a significant change in the composition of the bacterial community in the aeration ponds. This study demonstrated that several advanced analytical methods are necessary to characterize the bacterial population in complex environments, such as the industrial aeration ponds.
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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 | 0.000 | 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 | 0.001 | 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".