Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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 it