Surface irrigation of dairy farm effluent. Part I : Nutrient and Bacterial Load
Bibliographic record
Abstract
When handled separately from manures, dairy farm effluents (DE) are costly to manage because of their low nutrient content and large volume. In anticipation of using surface irrigation to lower the land application cost of DE, this projects investigated the impact, on DE characteristics, of different sources and storage systems.\nMilk house wastewaters were monitored on two farms using a 500 l manhole intercepting these before entering the septic tank. Manure runoff, with and without milk house wastewater, was also characterised on six farms for 1 yr, and on two farms for three consecutive years, where each farm used a different management system.\nThe results indicated that DE containing milk house wastewater, manure runoff or a mixture of both had a relatively low nutrient load, confirming that their application rate needed to range between 205 and 2050m3 ha-1, depending on the management system used. Stored along with solid manure, DE generally had a higher total solids (TS), nutrient loads and bacterial count, as compared to that drained away from the solid manure. Furthermore, for effluent drained away from solid manures, rainfall rather than snow occurring from October to May, inclusively, tended to increase their TS and nutrient load. The ratio of faecal coliforms to faecal streptococci (FC/FS) was greater than 10 when milk house wastewaters were stored along with the manure runoff because milk house wastewaters increased the death rate for FS compared to FC.
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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 | 0.000 |
| 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".