Investigation on the temporal variation and source tracking of faecal bacteria in a forest dominated watershed (Comox Lake), British Columbia, Canada
Bibliographic record
Abstract
AIMS: The aims of this study were to investigate the temporal variation in Escherichia coli density and its sources at the drinking water intake of Comox Lake for a period of 3 years (2011-2013). METHODS AND RESULTS: Density of E. coli was assessed by standard membrane filtration method. Source tracking of E. coli were done by using BOX-A1R-based rep-PCR DNA fingerprinting method. Over the years, the mean E. coli density ranged from nondetectable to 9·8 CFU 100 ml(-1) . The density of E. coli in each of the years did not show any significant difference (P > 0·05); however, a comparatively higher density was observed during the fall. Wildlife was (64·28%, 153/238) identified as the major contributing source of E. coli, followed by human (18·06%, 43/238) and unknown sources (17·64%, 42/238). Although the sources were varied by year and season, over all, the predominant contributing sources were black bear, human, unknown, elk, horse and gull. CONCLUSIONS: The findings of this investigation identified the multiple animal sources contributing faecal bacteria into the drinking water intake of Comox Lake and their varying temporal occurrence. SIGNIFICANCE AND IMPACT OF THE STUDY: The results of this study can reliably inform the authorities about the most vulnerable period (season) of faecal bacterial loading and their potential sources in the lake for improving risk assessment and pollution mitigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".