Assessment of the Waste-to-Energy Potential from Alberta’s Food Processing Industry.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 it