Alternatives for animal drinking and barn cleaning to reduce water use in swine facilities
Bibliographic record
Abstract
Animal drinking and barn cleaning are activities in swine barns where potential water saving can be achieved. In this study, selected water conservation strategies involving animal drinking and barn cleaning were assessed for their effectiveness in reducing the overall water use. For animal drinking, three types of drinkers were investigated: nipple (Control), nipple with side panel, and a trough with side panel and constant water level. The drinkers were distributed randomly among pens in a pig room and their impact on water use, water wastage, and pig performance were assessed throughout one complete grow-finish cycle. Results showed that relative to conventional nipple drinkers, the use of a drinking trough with side panel and constant water level saved about 60% of water through reduced water wastage without adversely affecting pig performance throughout the growth cycle. Water wastage and water disappearance rates increased as pigs reach market weights. For cleaning, on the other hand, experiments evaluating the effect of the use of water sprinkling (pre-soaking) and different high-pressure washing nozzles on water and time consumption in pig rooms with fully slatted flooring and partially slatted flooring revealed that the use of the conventional rotating turbo nozzle led to lesser time and water consumption during high-pressure washing. Also, high-pressure washing in rooms with fully slatted flooring can be done without prior water sprinkling. Economic analysis of the different measures showed that compared to current conventional practices, the combination of using a drinking trough with side panel and constant water level for animal drinking and pre-soaking and high-pressure washing with conventional nozzle for cleaning had the greatest potential for cost savings of up to C$4.77 per pig arising from reduced overall water use and accumulated manure slurry.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".