Microbial Diversity and Host-Specific Sequences of Canada Goose Feces
Bibliographic record
Abstract
Methods to assess the impact of goose fecal contamination are needed as the result of the increasing number of Canada geese (Branta canadensis) near North American inland waters. However, there is little information on goose fecal microbial communities, and such data are important for the development of host-specific source-tracking methods. To address this issue, 16S rRNA gene clone libraries for Canada goose fecal samples from Ontario, Canada, and Ohio were analyzed. Analyses of fecal clones from Ontario (447) and Ohio (302) showed that goose fecal communities are dominated by the classes "Clostridia" (represented by 33.7% of clones) and "Bacilli" (38.1% of clones) and the phylum "Bacteroidetes" (10.1% of clones). Sequences not previously found in other avian fecal communities were used to develop host-specific assays. Fecal DNA extracts from sewage plants (10 samples) and different species of birds (11 samples) and mammals (18 samples) were used to test for host specificity. Of all the assays tested, one assay showed specificity for Canada goose fecal DNA. The PCR assay was positive for Canada goose fecal DNA extracts collected from three locations in North America (Ohio, Oregon, and Ontario, Canada). Additionally, of 48 DNA extracts from Lake Ontario waters presumed to be impacted by waterfowl feces, 19 tested positive by the assay, although 10 were positive only after a nested PCR approach was used. Due to the level of host specificity and the presence of signals in environmental waters, the assay is proposed as a part of the toolbox to detect Canada goose contamination in waterfowl-contaminated 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 | 0.001 | 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".