Phylogenetic Diversity and Molecular Detection of Bacteria in Gull Feces
Bibliographic record
Abstract
In spite of increasing public health concerns about the potential risks associated with swimming in waters contaminated with waterfowl feces, little is known about the composition of the gut microbial community of aquatic birds. To address this, a gull 16S rRNA gene clone library was developed and analyzed to determine the identities of fecal bacteria. Analysis of 282 16S rRNA gene clones demonstrated that the gull gut bacterial community is mostly composed of populations closely related to Bacilli (37%), Clostridia (17%), Gammaproteobacteria (11%), and Bacteriodetes (1%). Interestingly, a considerable number of sequences (i.e., 26%) were closely related to Catellicoccus marimammalium, a gram-positive, catalase-negative bacterium. To determine the occurrence of C. marimammalium in waterfowl, species-specific 16S rRNA gene PCR and real-time assays were developed and used to test fecal DNA extracts from different bird (n = 13) and mammal (n = 26) species. The results showed that both assays were specific to gull fecal DNA and that C. marimammalium was present in gull fecal samples collected from the five locations in North America (California, Georgia, Ohio, Wisconsin, and Toronto, Canada) tested. Additionally, 48 DNA extracts from waters collected from six sites in southern California, Great Lakes in Michigan, Lake Erie in Ohio, and Lake Ontario in Canada presumed to be impacted with gull feces were positive by the C. marimammalium assay. Due to the widespread presence of this species in gulls and environmental waters contaminated with gull feces, targeting this bacterial species might be useful for detecting gull fecal contamination in waterfowl-impacted waters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".