Source tracking of escherichia coli in a freshwater lake in northwestern ontario (Boulevard Lake, Thunder Bay)
Bibliographic record
Abstract
Escherichia coli is regularly used as a fecal indicator bacteria (FIB) in recreational waters but its persistence in the environment brings its use into doubt. A secondary FIB known as Bacteroides has been under a lot of research lately. Because Bacteroides can be measured with quantitative PCR (qPCR) techniques easily, this makes it an effective FIB to detect fecal contamination. Boulevard Lake in Thunder Bay, Ontario experiences instances of elevated levels of E. coli throughout the summer seasons. For the 2011 season the Bacteroides 16S rDNA markers were monitored and compared with the E. coli population. As both of these FIB are found in feces, influxes of fecal contamination would see increases in both populations. The planktonic E. coli population densities exceeded the Canadian Recreational Water Quality Guidelines of 2.30 log CFU 100ml-1 two times throughout the summer season. These were measured at 2.86 and 2.38 log CFU 100 ml-1 on July 21, 2011 and September 2, 2011, respectively. The Bacteroides biomarkers did have any significant increases during these peak periods of E. coli with p > 0.05. This would suggest that the increased levels of E. coli may not have been due to fecal contaminants. Further investigations with a microbial source tracking approach will provide insights to the potential source(s) of E. coli in Boulevard Lake. For both 2010 and 2011, the planktonic E. coli population at Boulevard Lake was monitored.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".