Real-Time PCR Detection and Quantification of Nine Potential Sources of Fecal Contamination by Analysis of Mitochondrial Cytochrome <i>b</i> Targets
Bibliographic record
Abstract
We designed and tested real-time PCR probe/primer sets to detect and quantify Cytochrome b sequences of mitochondrial DNA (mtDNA) from nine vertebrate species of pet (dog), farm (cow, chicken, sheep, horse, pig), wildlife (Canada goose, white-tailed deer), and human. Linear ranges of the assays were from 10(1) to 10(8) copies/microl. To formally test the performance of the assays, twenty blinded fecal suspension samples were analyzed by real-time PCR to identify the source of the feces. Sixteen of the twenty samples were correctly and unambiguously identified. Average sensitivity was calculated to be 0.850, while average specificity was found to be 0.994. One beef cow sample was not detected, but mtDNA from 11 other beef cattle of both sexes and varying physiological states was found in concentrations similar (3.45 x 10(7) copies/g) to thatfound in human feces (1.1 x 10(7) copies/g). Thus, environmental conditions and sample handling are probably important factors for successful detection of fecal mtDNA. When sewage samples were analyzed, only human mtDNA (7.2 x 10(4) copies/ 100 mL) was detected. With a detection threshold of 250 copies/reaction, an efficient concentration and purification method resulted in a final detection limit for human feces of 1.8 mg/ 100 mL water.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".