Greenhouse Gas Emissions from Three Cage Layer Housing Systems
Bibliographic record
Abstract
Agriculture accounts for 10 to 12% of the World's total greenhouse gas (GHG) emissions. Manure management alone is responsible for 13% of GHG emissions from the agricultural sector. During the last decade, Québec's egg production systems have shifted from deep-pit housing systems to manure belt housing systems. The objective of this study was to measure and compare carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) emissions from three different cage layer housing systems: a deep liquid manure pit and a manure belt with natural or forced air drying. Deep liquid manure pit housing systems consist of "A" frame layer cages located over a closed pit containing the hens' droppings to which water is added to facilitate removal by pumping. Manure belt techniques imply that manure drops on a belt beneath each row of battery cages where it is either dried naturally or by forced air until it is removed. The experiment was replicated with 360 hens reared into twelve independent bench-scale rooms during eight weeks (19-27 weeks of age). The natural and forced air manure belt systems reduced CO₂ (28.2 and 28.7 kg yr(-1) hen(-1), respectively), CH₄ (25.3 and 27.7 g yr(-1) hen(-1), respectively) and N₂O (2.60 and 2.48 g yr(-1) hen(-1), respectively) emissions by about 21, 16 and 9% in comparison with the deep-pit technique (36.0 kg CO₂ yr(-1) hen(-1), 31.6 g CH₄ yr(-1) hen(-1) and 2.78 g N₂O yr(-1) hen(-1)). The shift to manure belt systems needs to be encouraged since this housing system significantly decreases the production of GHG.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".