Assessment of the Waste-to-Energy Potential from Alberta’s Food Processing Industry.
Bibliographic record
Abstract
Alberta’s food processing industry is the second largest food waste producer after the household sector. Most of the waste currently produced from the food processing industry is landfilled. Decomposing landfill waste, moreover, emits greenhouse gases (GHG), which contribute to global warming. In this paper, we estimated the amount of food waste produced by Alberta’s food processing industry by developing a geographical information system (GIS)-based model with data from food processing companies in the province. The companies were selected such that all sizes, types, and geographic locations were considered. The information was gathered on the amount and characteristics of food waste, the location of the processing facilities, and the food waste disposal method and then the total amount of food waste generated in Alberta was estimated. In addition, GIS maps were created to show the distribution of food waste throughout the province and the availability intensity. Finally, we estimated the potential energy that could be produced in the form of biogas and electricity using Alberta’s food processing waste and mapped it as well. There is a potential to generate 852 GWh of electricity per year from Alberta’s food processing waste, which is about 1% of the province’s total electricity generation. This potential capacity could help in the development of waste-to-value-added facilities in Alberta.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".