Bibliographic record
Abstract
The development of new energy has attracted consideration attention due to the high oil price and environmental problems. In advanced country, they have tried to carry out a long range plan for energy. We need to develop new energy for Low Carbon Green Growth in Korea. The building is 30% among ratio of energy consumption in Korea. And in the past, heating energy was high ratio for energy using at home. But recently, the demand for cooling energy keeps growing due to rising average temperature on the earth and improvement of life quality. In this situation, the energy of lake water and ocean water has studied to utilize in advanced country because of low temperature at underwater. But the study for deep water is still a lot left to do. In this study, we analyzed district cooling system and the present condition. Analyzing the deep lake water cooling system in Toronto, we found an application of district cooling system using deep ocean water. Deep lake water uses heat source for district cooling and water source for city in Toronto. So reducing the initial cost, this city had economic effect. When DLWC was applied at existing building, the heat exchanger was installed instead of cooling tower and refrigerator. And the heat exchanger used to connect main pipe with cool water on city. System using deep ocean water can be applied as a similar way to supply cool water from lake to building.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".