Horse Power: Turning Manure into Power for Small Acreages using a Biodigester
Bibliographic record
Abstract
Rural landscapes in Alberta are changing. The number of small acreages is growing, and these new rural residents often need, and benefit from, resources on how best to manage their land and animals. Rural residents have a direct impact on the natural environment and should, therefore, practice land stewardship. Land stewardship is the act of taking care of the land you own in a way that benefits the natural environment. Proper manure management is one example of how small acreage owners can practice land stewardship, because poorly managed manure piles can negatively affect water quality (Warren & Sweet, 2003). For example, runoff from manure piles can carry excess nutrients, pathogens, and organic material into groundwater (Warren & Sweet, 2003). In addition, manure piles can become a breeding ground for flies and other insects (Warren & Sweet, 2003). Considering that the average 450 kg horse produces 16.65 kg of feces per day (Westendorf, 2019), knowing what to do with the manure and understanding the potential benefits of feces (i.e. biogas production) can go a long way in helping the environment.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".