A review of the environmental pollution originating from the piggery industry and of the available mitigation technologies: towards the simultaneous biofiltration of swine slurry and methaneThis article is one of a selection of papers published in this Special Issue on Biological Air Treatment.
Bibliographic record
Abstract
In Canada, the piggery industry is an essential part of the agricultural sector, but the main waste product of this industry, swine slurry, is particularly harmful to the environment. The anaerobic storage conditions and the excessive use of slurry for agricultural fertilization contribute, respectively, to the emission of greenhouse gases and to aquatic pollution. This paper provides a review of these environmental concerns and of the existing mitigation technologies. Water pollution from swine slurry is associated with the nutrients it contains, such as nitrogen and phosphorous, while the main greenhouse gases produced by the piggery industry are methane and nitrous oxide. Available technologies can valorize the slurry through agricultural fertilization, reduce greenhouse gas emissions, by limiting nutrient availability for example, or treat the effluents using solid–liquid separation, flaring or biological processes. Specific attention is paid to biofiltration due to its potential to simultaneously treat these two types of pollution.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".