<i>Escherichia coli</i> Populations in Great Lakes Waterfowl Exhibit Spatial Stability and Temporal Shifting
Bibliographic record
Abstract
Populations of Escherichia coli from juvenile and adult ring-billed gulls, juvenile common terns, and adult Canada geese were sampled over 6 years at five locations on Lake Superior (Duluth, MN, and Wisconsin) and Lake Michigan (Wisconsin, Illinois, and Indiana) to determine the extent of spatial and temporal variability in E. coli strains. Strain identity was determined using horizontal fluorophore-enhanced repetitive element palindromic DNA fingerprinting. Multivariate statistics were used to determine if spatial or temporal changes in E. coli populations occurred in waterfowl species. Pairwise multivariate analyses of variance revealed that E. coli populations of adult gulls from three regions of Lake Michigan and the Wisconsin shore of Lake Superior were similar to one another but different from an E. coli population of gulls from the Duluth region of Lake Superior. Juvenile and adult gulls from the Duluth area harbored different E. coli populations. The E. coli strains from juvenile gulls, however, were similar to those found in juvenile terns obtained from the same island rookery. Temporal changes in E. coli populations from several waterfowl species were also demonstrated for this site. Although portions of source tracking databases might be successfully used in other geographic regions, it is clear that juvenile birds should not be the sole source of E. coli strains used for source tracking databases, and multiple-year libraries should be constructed in order to identify the potential sources of E. coli in the environment.
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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".