Bibliographic record
Abstract
Climate change is one of the greatest challenges of the 21st century. One of its effects will be hotter summers and therefore an increased demand for indoor cooling systems. Unfortunately, these cooling systems can exacerbate climate change in two ways. The first is that these systems require a large amount of electricity to generate the cooling load; this electricity is currently made from fossil fuel burning power plants. The second, is they require potent greenhouse gases, in the form of refrigerants, to circulate within the system; these gases are often released into the atmosphere at the end of the system’s life. Alternative systems that are less energy intensive and make use of cleaner resources can help alleviate these issues. This research explores how stored snow can be used as a cooling source for buildings. A feasibility study was conducted for Edmonton to assess the viability of these snow cooling systems in Canada. Snowfall and weather statistics were gathered to find whether Edmonton has enough snow and the right weather conditions for the implementation of these cooling systems. One of the important findings was how previous research on the preservation of snow in alpine ski resorts could be applied to these systems, resulting in more efficient snow storage management. The implication from this research is that these cooling systems could reduce carbon emissions, and alleviate the challenges of climate change. Department: Engineering Faculty Mentor: Dr. Jeff Davis
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".