Do volatile solids from bedding materials increase greenhouse gas emissions for stored dairy manure?
Bibliographic record
Abstract
Current approaches for estimating greenhouse gas (GHG) emissions from manure storages do not consider contributions due to bedding materials. Compared with sand, wood-based bedding has the potential to increase volatile solids and total solids concentrations and favour crust formation in liquid dairy manure. In this study, the GHG emissions from wood and sand bedding slurries were evaluated monitored continuously for 207 d (1 May–24 Nov. 2014) under “warm season” storage conditions. For both slurries, methane (CH4) made up >95% of the GHG emissions. The sand bedding slurry had minimal crust, which also led to more evaporation and higher ammonia volatilization losses when scaled by nitrogen content. The wood bedding slurry emitted 51% more CH4, eight times more nitrous oxide, and 53% more total GHG emissions (CO2-eqivalents). However, these differences were reduced if only the initial 123 d (1 May–31 Aug. 2014) of storage was considered. This was presumably related to the slower degradability of the wood bedding. Given these differences bedding choice should be considered in GHG emissions estimates.
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) | 0.000 | 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 | 0.001 |
| 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".