Windrow temperatures and chemical properties during active and passive aeration composting of beef cattle feedlot manure
Bibliographic record
Abstract
Windrow composting emerged in the mid-1990s as an alternative manure-handling practice in Alberta’s cattle feedlot industry. This study compared two composting methods: active (turning) and passive aeration. Temperatures were monitored over the first 90 d and chemical properties over 188 d of composting. Pre- vs. post-turning sampling of the active treatment was also compared. Mean daily temperature was warmest at the bottom windrow location (53.6ºC) and coolest at the top (46.4ºC) in the active treatment, but warmest at the top (44.1ºC) and coolest at the bottom (33.9ºC) in the passive treatment. Final compost from the passive treatment had significantly higher total N (TN), total C (TC), electrical conductivity (EC), Na, and Cl than the active treatment. There were no significant treatment effects on C:N ratio, NH4-N, NO3-N, total P (TP), Kelowna-extractable P (KEP), pH, Ca, Mg or K. Both treatments showed substantial and non-significantly different C (71–80%) and N (44–58%) losses. Pre- versus post-turning sampling showed significant differences for some compost parameters, notably soluble salts. After the thermophilic phase, the passive treatment appeared only partially composted. Additional disadvantages of the passive treatment included lower windrow temperatures, which may fail to reduce pathogens, and higher EC, which could potentially limit the end use of passively aerated compost. Key words: Cattle manure, composting, active aeration, passive aeration, nutrients
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 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".