Nutrient and trace element changes during manure composting at four southern Alberta feedlots
Bibliographic record
Abstract
Interest in composting as a means of handling the large volumes of manure generated by southern Alberta beef cattle feedlots has increased in recent years. We measured concentrations of 19 elements (C, N, P, Na, Ca, Mg, K, S, Al, Fe, Mn, Zn, Cu, Cr, Ni, P b , Co, Mo and Cd) in fresh manure, interim-composted manure and finished compost, at four commercial feedlots. Thirteen elements showed increased concentrations (by 26–73%) with composting, while four (C, Cr, Ni and Mo) showed concentration declines. Of the remaining two, the trend in N concentration was feedlot dependent, while Pb was largely unaffected. Total mass loss during composting averaged 54%, which represents a substantial decrease in haulage requirements. Overall average C losses were 61% and N losses 33%. On an equivalent wet weight basis ("as-is"), composting allowed haulage of 56% more N, 84% more P, 91% more Zn, and 76% more Cu than fresh manure, which is advantageous in terms of moving nutrients and trace elements from high to low-loading areas. Our study quantifies nutrient and trace element behaviour during composting, provides comparative data with fresh manure, and helps tailor end-use decisions (e.g., haulage distance, application rate) on the compost product. Key words: Manure, compost, beef cattle feedlots, nutrients, trace elements
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".