The Extent of Manure Removal from Storages and Its Impact on Gaseous Emissions
Bibliographic record
Abstract
Manure remaining in storage due to incomplete removal is a source of microbial inoculum that may affect methane (CH4), nitrous oxide (N2O), and ammonia (NH3) emissions during subsequent storage. Manure removal was studied by loading fresh manure into outdoor concrete tanks (10.6 m3) that contained previously stored manure (inoculum) at six levels (0, 5, 10, 15, 20, and 25%, with 0% representing an empty tank). Emissions were continuously measured for 6‐mo storage periods (warm and cold seasons) using flow‐through chambers. Fluxes during the warm season (average manure temperature at 80 cm depth, Tm = 17°C) were 25 times higher for CH4, 20 times higher for N2O, and 2.9 times higher for NH3 compared with the cold season (Tm = 4°C). Cumulative CH4 emissions increased linearly with the level of added inoculum in the cold season (r2 = 0.98). A similar linear increase was observed in the warm season from 0 to 20% inoculum (r2 = 0.91), after which a decrease in emissions was observed at 25%. Reducing inoculum from 15 to 5% reduced CH4 emissions by 26% in the warm season and 45% in the cold season. There was no clear effect of inoculum on N2O and NH3 emissions, suggesting that complete manure storage emptying does not alter their emissions. Core Ideas Manure in storage from incomplete removal is a source of microbial inoculum affecting CH4 emissions. Cumulative CH4 emissions increased linearly with the level of added inoculum in the cold season. Cumulative CH4 emissions increased linearly from 0 to 20% inoculum in the warm season. Reducing inoculum from 15 to 5% reduced CH4 emissions in warm season by 26% and cold season by 45%.
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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 | 0.001 | 0.000 |
| 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".