Comparison of Gull Feces-Specific Assays Targeting the 16S rRNA Genes of Catellicoccus marimammalium and Streptococcus spp
Bibliographic record
Abstract
Two novel gull-specific quantitative PCR (qPCR) assays were developed using 16S rRNA gene sequences from gull fecal clone libraries: a SYBR green assay targeting Streptococcus spp. (gull3) and a hydrolysis TaqMan assay targeting Catellicoccus marimammalium (gull4). The objectives of this study were to compare the host specificity of a previous C. marimammalium qPCR assay (gull2) with that of the new markers and to examine the presence of the three gull markers in environmental water samples from different geographic locations. Most of the gull fecal samples tested (n = 255) generated positive signals with the gull2 and gull4 assays (i.e., >86%), whereas only 28% were positive with gull3. Low prevalence and abundance of tested gull markers (0.6 to 15%) were observed in fecal samples from six nonavian species (n = 180 fecal samples), whereas the assays cross-reacted to some extent (13 to 31%) with other (nongull) avian fecal samples. The gull3 assay was positive against fecal samples from 11 of 15 avian species, including gull. Of the presumed gull-impacted water samples (n = 349), 86%, 59%, and 91% were positive with the gull2, the gull3, and the gull4 assays, respectively. Approximately 5% of 239 non-gull-impacted water samples were positive with the gull2 and the gull4 assays, whereas 21% were positive witg the gull3 assay. While the relatively high occurrence of gull2 and gull4 markers in waters impacted by gull feces suggests that these assays could be used in environmental monitoring studies, the data also suggest that multiple avian-specific assays will be needed to accurately assess the contribution of different avian sources in recreational 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".