On-farm Renewable Energy Projects for Greenhouse Gas Mitigation
Bibliographic record
Abstract
Five projects on renewable energy, partially supported by an R & D support program of Agriculture and Agri-Food Canada (AAFC) in collaboration with Natural Resources Canada, are described briefly. The AAFC Program on Energy Cogeneration from Agricultural and Municipal Wastes was aimed at mitigation of greenhouse gases. Four projects were supported to help establish farm-scale demonstration plants for anaerobic digestion of hog and beef cattle manure to produce biogas for energy cogeneration, and concentration of nutrients in digested manure. A fifth project was supported for gasification of agricultural waste straw and sorted municipal wastes. The technical, economic, energy, and environmental benefits derived from the projects resulting from the collaborative efforts of industry, academia, and regional, provincial and federal government are discussed along with the feedback received from the project proponents on project implementation. For long-term sustainability of renewable energy projects, it is very important that the governments provide incentives in the form of tax subsidies, guaranteed low cost loans, and/or guaranteed minimum prices for green energy because a considerable capital investment is required to set up such facilities. Incentives comparable to those received by fossil fuel industry should be available to build plants that utilize agricultural and municipal wastes for energy generation. Without such incentives, further development and adoption of the technology would be delayed. With the recent government initiatives in support of renewable energy in both Canada and the U.S., the future looks very promising.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".