Ground Source Heat Pumps versus High Efficiency Natural Gas Furnaces in Alberta
Bibliographic record
Abstract
Ground Source Heat Pumps (GSHPs) have been used for heating and cooling buildings in northern Europe for a couple of decades and at least a decade in eastern Canada and the USA. There are far fewer GSHP installations in the Canadian prairies than in eastern Canada. Natural gas furnaces (80 % efficient or less) dominate prairie homes because High Efficiency Natural Gas furnaces (HENGs:>90 % efficient) are more expensive. Major builders of new homes in the Edmonton area still install 80 % efficient furnaces most of the time. GSHPs exchange heat with the ground under a building. In the winter the heat is collected in a plastic pipe that either runs horizontally under the frost line in the soil or vertically in a series of drilled holes. The pipe is filled with a thermal fluid that has its temperature maintained lower than that of the surrounding soil. Heat flows from higher temperatures to lower temperatures. Inside the building there is a machine (water furnace, etc) which collects the heat from the ground pipe and relays it into the building heat distribution system. In hot summers the GSHP system operates in reverse by taking unwanted heat from the building and relaying it into the ground. GSHPs currently have efficiencies (Coefficient of Performance=COP) up to about 500%, depending on whether they are cooling or heating. No fossil fuel is used except in the production of the
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".