Energy and Nutrient Recovery from Cattle Feedlots
Bibliographic record
Abstract
Selective harvesting of manure can benefit cattle producers by creating a product of value. A tool that identifies locations of manure accumulation has been developed and demonstrated. A dual geometry sub-surface sensor (Dualem-1S, Milton, ON, Canada) was used with software designed for salt mapping (ESAP, Riverside, CA). The combination allowed the development of models to estimate a higher heating value (HHV) of feedlot manure across a feedlot pen. Soil sample data from cattle feedlots in Texas and Nebraska were analyzed for volatile solids (VS) and then combined with the Dualem-1S apparent soil electrical conductivity (ECa) data to produce models having correlations between ECa and associated VS (r2=0.869). A corresponding model is under development to estimate the moisture content of the collectable solids. The combined models allow real-time spatial estimates of HHV within a feedlot pen. These methods will allow direct harvesting of VS for use as a recoverable energy source through direct combustion or co-combustion.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".