Inactivation of Escherichia coli During Storage of Irrigation Water in Agricultural Reservoirs
Bibliographic record
Abstract
: Microbial contamination of surface waters is one of the most important water quality issues affecting the agricultural sector in Nova Scotia, Canada. Most farmers who irrigate in the province draw their source water directly from streams and rivers. One mode of pathogen transmission is the irrigation of horticultural crops with contaminated water. The extended storage of irrigation water prior to crop application could minimize this risk. Natural inactivation processes could, depending on the source water characteristics and length of storage time, reduce bacteria levels to acceptable use standards. The purpose of this project was to study bacterial population dynamics, specifically those of Escherichia coli, in shallow irrigation water reservoirs. Experiments, involving a series of dialysis tube survival studies, were conducted from May through September of 2006 in an operational farm reservoir to examine microbial inactivation kinetics. It was found that E. coli populations in the cool, lower layer (depth ≈ 3 m) did not decline, while populations in the warm upper layer (depth ≈ 1.0 m) experienced significant reductions over a period of several days. An inactivation model was calibrated and used to develop conservative estimates of T90, T99, and T99.9 values for environmental conditions typical of the Annapolis Valley of Nova Scotia. If a shallow reservoir was well mixed to prevent gradients in temperature and dissolved oxygen, storage of irrigation water for a period of at least two weeks would reduce bacterial numbers by at least three logs during the growing season in Nova Scotia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".