Bibliographic record
Abstract
Korea is utilizing geothermal resources mainly in the bathing and swimming facilities with very few applications for industrial processes or space heating. It is estimated that geothermal capacity and annual utilization are 36.2MWt and 761TJ/year as compared to global capacity and annual utilization of 15,145MWt and 190,699 TJ/year. RETScreen software is a user's friendly tool for analyzing the technical and financial pre-feasibility of potential Renewable Energy (RE) projects that promotes the use of RE applications through the capacity building of planners, decision-makers and industries for successful implementation of RE projects. Strong ties between Canada and Korean organizations such as Korean Solar Energy Society (KSES) and the Korea Institute of Energy Research (KIER) exist for knowledge transfer about RETScreen. In this paper, an overview of RETScreen and its ground source heat pump (GSHP) model with a practical example of an existing project of a community hall in Canada are described to illustrate effectiveness of RETScreenin the implementation of RE technologies. The same community hall project is then evaluated hypothetically considering its location at Kangnyng, Korea. The main objective is to demonstrate how RETScreen GSHP model can also be utilized effectively for GSHP applications in Korea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".