Genetic Markers for Rapid PCR-Based Identification of Gull, Canada Goose, Duck, and Chicken Fecal Contamination in Water
Bibliographic record
Abstract
Avian feces contaminate waterways but contribute fewer human pathogens than human sources. Rapid identification and quantification of avian contamination would therefore be useful to prevent overestimation of human health risk. We used subtractive hybridization of PCR-amplified gull fecal 16S RNA genes to identify avian-specific fecal rRNA gene sequences. The subtracters were rRNA genes amplified from human, dog, cat, cow, and pig feces. Recovered sequences were related to Enterobacteriaceae (47%), Helicobacter (26%), Catellicoccus (11%), Fusobacterium (11%), and Campylobacter (5%). Three PCR assays, designated GFB, GFC, and GFD, were based on recovered sequence fragments. Quantitative PCR assays for GFC and GFD were developed using SYBR green. GFC detected down to 0.1 mg gull feces/100 ml (corresponding to 2 gull enterococci most probable number [MPN]/100 ml). GFD detected down to 0.1 mg chicken feces/100 ml (corresponding to 13 Escherichia coli MPN/100 ml). GFB and GFC were 97% and 94% specific to gulls, respectively. GFC cross-reacted with 35% of sheep samples but occurred at about 100,000 times lower concentrations in sheep. GFD was 100% avian specific and occurred in gulls, geese, chickens, and ducks. In the United States, Canada, and New Zealand, the three markers differed in their geographic distributions but were found across the range tested. These assays detected four important bird groups contributing to fecal contamination of waterways: gulls, geese, ducks, and chickens. Marker distributions across North America and in New Zealand suggest that they will have broad applicability in other parts of the world as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".