Carbon, Nitrogen Balances and Greenhouse Gas Emission during Cattle Feedlot Manure Composting
Why this work is in the frame
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Bibliographic record
Abstract
Carbon and N losses reduce the agronomic value of compost and contribute to greenhouse gas (GHG) emissions. This study investigated GHG emissions during composting of straw-bedded manure (SBM) and wood chip-bedded manure (WBM). For SBM, dry matter (DM) loss was 301 kg Mg(-1), total carbon (TC) loss was 174 kg Mg(-1), and total nitrogen (TN) loss was 8.3 kg Mg(-1). These correspond to 30.1% of initial DM, 52.8% of initial TC, and 41.6% of initial TN. For WBM, DM loss was 268 kg Mg(-1), TC loss was 154 kg Mg(-1), and TN loss was 1.40 kg Mg(-1), corresponding to 26.5, 34.5, and 11.8% of initial amounts. Most C was lost as CO2 with CH4 accounting for <6%. However, the net contribution to greenhouse gas emissions was greater for CH4 since it is 21 times more effective at trapping heat than CO2. Nitrous oxide (N2O) emissions were 0.077 kg N Mg(-1) for SBM and 0.084 kg N Mg(-1) for WBM, accounting for 1 to 6% of total N loss. Total GHG emissions as CO2-C equivalent were not significantly different between SBM (368.4 +/- 18.5 kg Mg(-1)) and WBM (349.2 +/- 24.3 kg Mg(-1)). However, emission of 368.4 kg C Mg(-1) (CO2-C equivalent) was greater than the initial TC content (330.5 kg Mg(-1)) of SBM, raising the question of the net benefits of composting on C sequestration. Further study is needed to evaluate the impact of composting on overall GHG emissions and C sequestration and to fully investigate livestock manure management options.
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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) | 0.000 | 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 it