Thermotolerant Coliforms Are Not a Good Surrogate for <i>Campylobacter</i> spp. in Environmental Water
Bibliographic record
Abstract
This study aimed to assess the importance of quantitatively detecting Campylobacter spp. in environmental surface water. The prevalence and the quantity of Campylobacter spp., thermotolerant coliforms, and Escherichia coli in 2,471 samples collected weekly, over a 2-year period, from 13 rivers and 12 streams in the Eastern Townships, Québec, Canada, were determined. Overall, 1,071 (43%), 1,481 (60%), and 1,463 (59%) samples were positive for Campylobacter spp., thermotolerant coliforms, and E. coli, respectively. There were weak correlations between the weekly distributions of Campylobacter spp. and thermotolerant coliforms (Spearman's rho coefficient = 0.27; P = 0.008) and between the quantitative levels of the two classes of organisms (Kendall tau-b correlation coefficient = 0.233; P < 0.0001). Well water samples from the Eastern Townships were also tested. Five (10%) of 53 samples from private surface wells were positive for Campylobacter jejuni, of which only 2 were positive for thermotolerant coliforms. These findings suggest that microbial monitoring of raw water by using only fecal indicator organisms is not sufficient for assessing the occurrence or the load of thermophilic Campylobacter spp. Insights into the role of environmental water as sources for sporadic Campylobacter infection will require genus-specific monitoring techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".