Feasibility analysis on diverting regional waste to local business for waste to energy project
Bibliographic record
Abstract
A commercial greenhouse fueled by community waste is a business model that could mitigate growing economic pressures on northern communities facing rising costs. A business initiative proposed by Woodmere Nursery (Woodmere) in Telkwa, BC, is to convert community waste into biomass for fuel to heat their greenhouses year round. Woodmere presented their proposal to the Regional District of Bulkley-Nechako (RDBN), who is currently transporting their municipal solid waste (MSW) to two landfills within the region. The aim of this project is to do a feasibility analysis of the RDBN diverting MSW to Woodmere for their proposed waste to energy project, a thermal oxidization processing system. A few key sources were used to complete the analysis. An existing RDBN True Cost Accounting Study done on the RDBN waste management process in April 2006, the RDBN five year budget, and literature from EnEco Industries Ltd, which is the supplier of the waste conversion equipment were reviewed. The analysis showed a cost benefit to the RDBN and a significant benefit to extending the life of the Knockholt landfill. Although not the focus of this analysis, this study also suggests the Woodmere project has merit. There are possibly other benefits from this project, such as reduced greenhouse gas (GHG) emissions, and possible revenue streams from the trading of carbon offsets, from the sale of MSW, and from the sale of excess energy. These are however, beyond the scope of this paper and are possible subjects for further research. --P. 2.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".