Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) emerged as alternative refrigerants after chloro fluorocarbons (CFCs) and hydro-chloro fluorocarbons (HCFCs) were identified as substances requiring control by the Montreal Protocol on Substances that Deplete the Ozone Layer. However, because the Kyoto Protocol considered HFCs as greenhouse gases, and their impact on climate change has been increasing, major developed countries have been strengthening the existing level of regulations related to the use of HFCs as refrigerants. In addition, South Korea has also passed various legislations relating to refrigerant management, in the form of policies such as the Wastes Control Act, the Act on Control etc. of the Manufacture of Specific Substances for the Protection of the Ozone Layer, the Clean Air Conservation Act, and the Act on Resource Circulation of Electrical and Electronic Equipment and Vehicles. However, reports indicate that these regulations have not been followed effectively due to the lack of a specific system relating to the phased management of production, use, and disposal of refrigerant materials. In order to identify and solve the problems relating to refrigerant management in South Korea, this study investigates the current state of refrigerant management in three separate phases: production, use, and disposal of refrigerants. Outstanding refrigerant management policies are also analyzed, using those enacted in the EU, United States, and Japan as examples, and these are then compared to regulations 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.
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 | 0.000 | 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) | 0.002 | 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".