BACTERIAL QUALITY OF SURFACE AND SUBSURFACE DRAINAGE WATER FROM MANURED FIELDS
Bibliographic record
Abstract
The Nova Scotia Water Quality Research Group at the Nova Scotia Agricultural College inTruro, Nova Scotia, Canada, monitors the environmental effects of agricultural operations inAtlantic Canada. One aspect of this research is the monitoring of surface and subsurfacedrainage water for manure-derived fecal coliforms and Escherichia coli. This occurs at farmswhere drainage water discharges into heated monitoring sheds containing tipping buckets, datarecorders and autosamplers. The effects of various farm practices on the movement of E. coliinto drainage water has been investigated. Bacterial levels are normally highest at the beginningof each drainage flow event and then decrease as the event progresses. The levels subsequentlyincrease with each new flow event. Viable fecal bacteria can be recovered in drainage watermany months after manure application. This pattern has been noted in both surface runoff andsubsurface drainage water. E. coli numbers in soil decline to non-detectable levels within four tofive months after a spring manure application on small scale plots and three to four months aftera summer application. Regrowth of E. coli occurs in all manure treated plots. Manure applicationtechnique does not appear to influence the population decline. Higher E. coli numbers appear inleachate when manure is applied under spring conditions as compared to summer conditions. Thetransport of E. coli is delayed when manure is incorporated into the soil as compared to when itis surface broadcast.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".