<i>Enterococcus</i> species diversity in fecal samples of wild marine species as determined by real-time PCR
Bibliographic record
Abstract
Analyses using culture-independent molecular techniques have improved our understanding of microbial composition. The aim of this work was to identify and quantify enterococci in fecal samples of wild marine species using real-time quantitative PCR. Seven Enterococcus species were examined in fecal DNA of South American fur seals (Arctocephalus australis), Subantarctic fur seals (Arctocephalus tropicalis), green turtles (Chelonia mydas), Magellanic penguins (Spheniscus magellanicus), snowy-crowned tern (Sterna trudeaui), white-backed stilt (Himantopus melanurus), white-chinned petrels (Procellaria aequinoctialis), red knot (Calidris canutus), and black-browed albatross (Thalassarche melanophris). All Enterococcus species evaluated were detected in all fecal samples of wild marine species, with a concentration ranging between 106 and 1012 copies/ng of total DNA. Differences in the enterococci distribution were observed. Enterococcus faecalis and Enterococcus mundtii were most abundant in marine mammals. Enterococcus faecalis was frequent in green turtle, Magellanic penguin, snowy-crowned tern, red knot, and black-browed albatross. Enterococcus hirae and Enterococcus gallinarum showed elevated occurrence in white-backed stilt, and Enterococcus faecium in white-chinned petrel. This study showed highest diversity of enterococci in feces of wild marine species than currently available data, and reinforced the use of culture-independent analysis to help us to enhance our understanding of enterococci in gastrointestinal tracts of wild marine species.
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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 | 0.000 |
| 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".