Bibliographic record
Abstract
The farming community regards agricultural wastes as resources. In addition to utilizing nutrientas fertilizer, green energy production is a well-known by-product. Examples include: anaerobicdigestion (AD) of livestock manure to produce biogas for use as fuel for stove, boiler, combinedheat and power (CHP) generation or even as vehicle fuel; and gasification of relatively dry wastessuch as poultry manure, wood chips, and saw dust for power generation. Besides green energy, processing of agricultural waste can provide many additional benefits.Utilization of carbon dioxide from combustion exhaust in greenhouses is less commonly known,and the environmental benefits such as the reduction of pathogens, odour, greenhouse gasemission and water contamination are difficult to quantify. These benefits are discussed in greaterdetail in this paper, and quantification methods are proposed. The overall cost of agriculturalwaste management can be significantly reduced when full-benefit accounting is applied, and thecost of implementing advanced technologies can be justified. An integrated solution foragricultural, environmental, public health and energy concerns can be developed by fully utilizingthe by-products. A recent case of locally initiated efforts in developing such an integrated solutionfor the rural community of Chatham-Kent in Ontario, Canada will be briefly described.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".