Converting food waste to usable energy in the urban environment through anaerobic digestion
Bibliographic record
Abstract
Urban sustainability is a subject of recent focus in North America, and Canada specifically. As our urban areas continue to grow and consume large quantities of energy and produce massive amounts of waste, we are faced with the challenge of how to manage this situation in a way which is both responsible and sustainable. One feasible urban waste-to-energy technology is anaerobic digestion. Anaerobic digestion (AD) has been a usable energy source for over 100 years and is currently being employed in several countries around the world in rural settings to generate electricity and heat, but it has yet to make a large migration to the urban environment even though it is a viable and mature process. Applied to the organic waste produced in urban environments, anaerobic digestion could provide a critical solution to growing garbage problems while simultaneously reducing external energy requirements. As landfills across Canada and the rest of the world are filled to their limits, a carbon-neutral process which can locally generate electricity and heat while providing up to 50% volatile solid reduction is something to be seriously considered. As fuel prices continue to inflate, the cost of transporting waste outside of cities to landfills will continue to rise. Large portions of this waste could be retained, digested, reduced, and converted into useable energy in the urban environment providing valuable energy. This paper investigates the feasibility of urban anaerobic digestion and provides a case study from the downtown campus of Concordia University in Montreal, Canada.
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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 | 0.000 | 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 | 0.000 | 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 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".