Does overwintering change the inoculum effect on methane emissions from stored liquid manure?
Bibliographic record
Abstract
Abstract Greenhouse gas (GHG) emissions, especially methane (CH4), from manure storage facilities can be substantial. Methane production requires adapted microbial communities (“inoculum”) to be present in the manure. Complete removal of liquid dairy manure (thus removing all inoculum) from storage tanks in the spring has been shown to significantly reduce CH4 emissions over the following warm season. This study examined whether the same mitigation effect would occur after fall removal of liquid dairy manure. Emissions of CH4, nitrous oxide (N2O), ammonia (NH3), and CO2 were measured from six 11.88‐m3 tanks equipped with flow‐through chambers. There were three inoculated controls (20% inoculum) and three uninoculated treatments, where inoculum was completely removed in the fall/winter (0% inoculum). Direct N2O and NH3 (indirect N2O) were minor contributors to the total GHG budget, contributing <2% on a CO2 equivalent (CO2e) basis. Removal of inoculum led to a 34% decrease in total emissions on a CO2e basis and to a 29% decrease in the CH4 conversion factor compared with the inoculated control (0.37 vs. 0.52; p = .01). Overall, removing inoculum in the fall reduced CH4 emissions from manure storage tanks; however, fall inoculum removal was less effective than in a previous study where inoculum was removed in the spring. The timing of inoculum removal may affect the efficiency of this CH4 mitigation strategy. However, this method may be impractical for larger manure storage tanks. Further study is required to overcome challenges of time‐sensitive, complete inoculum removal from farm‐scale storage tanks.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".