Comprehensive Evaluation of Using Solar Water Heater on a Household Scale in Canada
Bibliographic record
Abstract
Canadian researchers are now trying to exploit much more energy from solar sources, hydropower, wind, and biomass. Given the fact that reducing the carbon pollutant level is a political priority in Canada, this paper studies the feasibility of providing sanitary hot water and space heating demands of a four-member family in 10 provinces in this country. The feasibility analysis was performed by T*SOL Pro 5.5 software, and radiation data were obtained by MeteoSyn software. Results indicated that the most suitable station in terms of using solar water heater was Regina, which supplied 35 % of the total heat load for space heating and sanitary hot water purposes. This accounted for 5074 kWh of heat for space heating (25 % of demand) and 3112 kWh energy for sanitary hot water (94 % of demand) using a 40 m2 solar collector. In addition, results are indicative of an annual amount of saving up to 2080 kg of CO2 in the Regina station and an annual reduction of 984 m3 in natural gas for this station. In conclusion, Canada has a potentially alluring market to utilize solar water heaters for providing sanitary hot water for the residential sector.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".