An optimized enumeration method for sorbitol-fermenting <i>Bifidobacteria</i> in water samples
Bibliographic record
Abstract
With increased focus on watershed protection under the Surface Water Treatment Rule, indicators that discriminate among sources of microbial inputs (microbial source tracking) are needed to supplement the quantitative information provided by total and fecal coliform measurements for drinking water monitoring. Bifidobacteria are found in the digestive tract and feces of humans and other animals, and also in sewage. Sorbitol is a food additive used exclusively in food intended for human consumption. Therefore, the presence of sorbitol-fermenting Bifidobacteria in environmental waters can be indicative of sources of human fecal contamination. A series of media were evaluated using ATCC cultures of B. breve and B. adolescentis, feces from different animals, and domestic wastewater samples. The media evaluated were Human Bifid Sorbitol agar (HBSA), modified Human Bifid Sorbitol agar, Beerens Medium, modified Beerens Medium, Reinforced Clostridial agar, BIM-25 Medium, and modified BIM-25 Medium. Variables such as sample preservation, incubation time, different pH indicators, plating technique, and discontinuous exposure to sorbitol were also evaluated. A series of biochemical tests were used to confirm positive colonies enumerated on the various media. Membrane filtration and enumeration of sodium sulfite preserved samples on HBSA containing bromocresol purple using loose lidded plates for 48 h provided the best recoveries for presumptive positive colonies. A number of sorbitol-fermenters that were not Bifidobacteria were able to grow on all media tested, resulting in false-positives. Therefore, plating on HBSA should be followed by a confirmation step when monitoring for sorbitol-fermenting Bifidobacteria in environmental waters. A year-long sampling survey of a managed reservoir in Massachusetts provided field validation of the proposed methodology for sorbitol-fermenting Bifidobacteria as a human-related source tracking indicator tool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".