DNA-based real-time detection and quantification of aeromonads from fresh water beaches on Lake Ontario
Bibliographic record
Abstract
The present study was designed to develop a novel, rapid, direct DNA-based protocol to enumerate aeromonads in recreational waters. An Aeromonas genus-specific real-time quantitative polymerase chain reaction (Q-PCR) protocol was developed and optimized using newly designed genus-specific oligonucleotide primers derived from the gyrase B subunit (GyrB) gene. A standard curve was developed based on the PCR protocol with a minimum quantification limit of 10 cell equivalents ml(-1) achieved using an autoclaved water sample from recreational water spiked with known quantities of an Aeromonas ATCC strain. The Q-PCR protocol was validated and applied to detect and quantify the total number of aeromonads in water samples collected from two fresh water beaches on Lake Ontario. The Q-PCR protocol revealed significantly higher numbers of aeromonads in all water samples than a culture-based assay at both beaches. Foreshore sand was found to serve as a reservoir of high concentrations of Aeromonas similar to this phenomenon noted for enteric bacteria like Eschershia coli. The new real-time Q-PCR protocol facilitated the rapid quantification of total numbers of Aeromonas cells present in recreational water samples in <3 hours without culturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".