Soil respiration and nitrous oxide production in distillers grain and glycerol amended soil
Bibliographic record
Abstract
Applying by-products of biofuel production to soil may be an alternative use to take \ndirect advantage of nutrients and carbon contained within. Ethanol production results in \ndistiller grain and biodiesel produces glycerol as by-product. However, no information \ncurrently exists on the effects of adding these amendments on evolution of carbon \ndioxide and nitrous oxide from soils, yet is important to complete our understanding of \npotential impacts of biofuel production on greenhouse gas budgets as well as soil quality. \nPots containing soil amended with different rates of wet distillers grain, thin stillage, and \nglycerol were placed in incubation chambers and incubated for 10 days. Treatments of \nalfalfa powder and urea were added at the same rates of total N as the by-products for \ncomparative purposes. Carbon dioxide and nitrous oxide evolved from amended soil was \nmeasured. The alfalfa powder and wet distillers grain resulted in the greatest evolution of \nCO2 from the soil, with the thin stillage resulting in less CO2 evolved per unit of nitrogen \nadded. Addition of nitrogen fertilizer along with glycerol enhanced microbial activity and \ndecomposition. Per unit of nitrogen added, urea tended to result in the greatest N2O \nproduced, followed by wet distillers grain and thin stillage, with glycerol and dehydrated \nalfalfa resulting in the lowest nitrous oxide production.
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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) | 0.001 | 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) | 0.001 | 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".