Assessment of the microbial quality of irrigation water in a prairie watershed
Bibliographic record
Abstract
AIMS: To assess levels of faecal contamination in the Qu'Appelle River (Saskatchewan, Canada) and its suitability for irrigation, by using the Colilert-18/Quanti-Tray technology. METHODS AND RESULTS: Various sites located along the Qu'Appelle River were sampled weekly from May to August 2005-2007. A total of 594 freshwater samples were collected and analysed for enumeration of Escherichia coli using the Colilert-18. The false-positive rate for E. coli detection using Colilert-18 was at most 1.5%. Throughout the irrigation period (June to August), up to 85% of the water samples collected from one of the irrigation water-pumping sites exceeded the recommended limit of 100 CFU per 100 ml. Spikes in E. coli counts were generally concomitant with the sudden rise in river flows. A sub-sample of confirmed E. coli isolates were typed by randomly amplified polymorphic DNA (RAPD). RAPD analysis revealed a high degree of genetic diversity among E. coli isolates. A significant association between RAPD patterns and the month of E. coli isolation was demonstrated. CONCLUSIONS: Colilert-18 provides an effective means for assessing microbial quality of irrigation water. SIGNIFICANCE AND IMPACT OF THE STUDY: Qu'Appelle River is subject to variability of faecal contamination during irrigation times and monitoring throughout irrigation season is important for ensuring safe production practices.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".