Does overwintering change the inoculum effect on methane emissions from stored liquid manure?
Bibliographic record
Abstract
Abstract Greenhouse gas (GHG) emissions, especially methane (CH 4 ), 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 CH 4 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 CH 4 , nitrous oxide (N 2 O), ammonia (NH 3 ), and CO 2 were measured from six 11.88‐m 3 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 N 2 O and NH 3 (indirect N 2 O) were minor contributors to the total GHG budget, contributing <2% on a CO 2 equivalent (CO 2 e) basis. Removal of inoculum led to a 34% decrease in total emissions on a CO 2 e basis and to a 29% decrease in the CH 4 conversion factor compared with the inoculated control (0.37 vs. 0.52; p = .01). Overall, removing inoculum in the fall reduced CH 4 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 CH 4 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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".