Evaluation of water usage and water conservation strategies in the swine industry
Bibliographic record
Abstract
Water is a critical input in swine operations but often neglected because of the prevailing notion that water will always be available in unlimited quantity. However, excessive use of water can have negative impact on the environment and cause depletion of water resources. In swine operations, water is used for animal drinking, cooling, cleaning, and domestic consumption. The rate of water use from different stages of swine production has impact on the overall production cost. Poor production practices may lead to higher water consumption and increased manure slurry volume which needs further handling and treatment, representing added cost. The objectives of this study are to assess the water usage in different stages of pig production and to compile the available water conservation management practices. The applicability of these conservation measures in swine production operations in terms of technical viability, economic costs for implementation, and benefits to the overall operation will be assessed. Preliminary results from this work included calculation of the current rate of water usage to produce each pig based on the literature review and survey of swine producers in Saskatchewan. Furthermore, the different technologically-feasible water conservation practices that pork producers can implement in their operations to reduce their water usage were ranked.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".