Microbial community dynamics of surface water in British Columbia, Canada
Bibliographic record
Abstract
Abstract Traditional methods for monitoring the microbiological quality of water focus on the detection of fecal indicator bacteria such as Escherichia coli , often tested as a weekly grab sample. To understand the stability of E.coli concentrations over time, we evaluated three approaches to measuring E. coli levels in water: microbial culture using Colilert, quantitative PCR for uidA and next-generation sequencing of the 16S rRNA gene. Two watersheds, one impacted by agricultural and the other by urban activities, were repeatedly sampled over a simultaneous ten-hour period during each of the four seasons. Based on 16S rRNA gene deep sequencing, each watershed showed different microbial community profiles. The bacterial microbiomes varied with season, but less so within each 10-hour sampling period. Enterobacteriaceae comprised only a small fraction (<1%) of the total community. The qPCR assay detected significantly higher quantities of E. coli compared to the Colilert assay and there was also variability in the Colilert measurements compared to Health Canada’s recommendations for recreational water quality. From the 16S data, other bacteria such as Prevotella and Bacteroides showed promise as alternative indicators of fecal contamination. A better understanding of temporal changes in watershed microbiomes will be important in assessing the utility of current biomarkers of fecal contamination, determining the best timing for sample collection, as well as searching for additional microbial indicators of the health of a watershed.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".