Waterfowl Abundance Does Not Predict the Dominant Avian Source of Beach <i>Escherichia coli</i>
Bibliographic record
Abstract
The horizontal, fluorophore enhanced, rep-PCR (HFERP) DNA fingerprinting technique was used to identify potential sources of in water, nearshore sand, and sediment at two beaches in the Duluth-Superior Harbor, near Duluth, MN, and Superior, WI, during May, July, and September 2006. An animal or environmental source could be identified for 35, 29, and 30% of strains in water, sand, and sediments, respectively. Waterfowl, including Canada geese, ring-billed gulls, and mallard ducks, were the largest source of that could be identified in water (55-100%), sand (59-100%), and sediment (92-100%) at both beaches. Although ring-billed gulls were more abundant in this harbor, Canada geese were usually the dominant source of waterfowl found at these beaches. The percentage of identified from treated wastewater was always less than the percentage of originating from waterfowl. At both beaches, the percentage of in water contributed by treated wastewater was higher in May compared with July and September. The larger proportion of wastewater-derived seen in May probably reflected a smaller contribution of from geese when these birds were less abundant rather than an absolute increase in from treated wastewater. Microbial source analysis and bird census data both indicated that waterfowl were a major source of at beaches in the Duluth-Superior Harbor. These data also indicated it is risky to assume that the most abundant waterfowl species present in waterways will also be the largest source of avian-derived in water, nearshore sand, and sediments at beaches.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".