Financial viability of switching propane heat to Combined Heat and Power (CHP) in rural northern communities
Bibliographic record
Abstract
Community energy production will serve as an avenue for self-sufficiency and energy independence from utilization of biomass in local forests. The most opportune way of achieving this is to maximize the full utilization of the forest resource. As in the rural northern communities, the forests are available for Combined Heat and Power (CHP) projects. The CHP technology is well established in Europe, and it would be beneficial for Canada to become business partners with the CHP manufacturers to ensure the knowledge and expertise is established in British Columbia (BC). Based on this project, the CHP system is most appropriate when the unit is producing enough heat and power for the facility rather than to establish a large CHP for selling the surplus power to BC Hydro. The BC Hydro bioenergy rates are not high enough for building a strong business case for CHP at a smaller scale. The rates for BC Hydro are a driver for CHP, if the project is built based on present energy prices the renewable energy based projects are more costly than fossil fuel burning energy systems. As shown in the capital budgeting calculations, the smaller BG25 has a greater chance of success due to the lower costs for the initial investment and the lower operating costs. When the purchase price of renewable energy increases and given priority with premium prices then CHP will be financially viable. --P. 2.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".