Application of natural refrigerants in the industrial refrigeration and heat pumps in the future
Bibliographic record
Abstract
For more than 150 years natural refrigerants have been used in various industrial applications reaching temperatures from -273°C to more than 300°C and the level of the high temperature limit is difficult to estimate at the time of writing this paper.\nAmmonia, NH3, R-717 has been the preferred refrigerant for normal everyday industrial refrigeration in strong competition with hydrochlorofluorocarbon (HCFC) R-22.i\nIn other applications carbon dioxide, CO2, R-744 used to be one of the preferred working fluids in the early days of refrigeration. In the 1980’s it became apparent that chlorine, Cl, was destroying the ozone layer that protects all life on earth from the dangerous ultraviolet (UV) rays emitted by the sun. The discussion leading up to the Montreal Protocol elaborated on what would be the right way to choose if chlorinated and fluorinated hydrocarbons were not the way to go. Many alternative fluids were suggested, and Gustav Lorentzen suggested to reconsider carbon dioxide, which he saw in operation in his young days when sailing as marine engineer.\nOthers suggested hydrocarbons, which are a large family of various types of natural occurring gasses with many different properties. Hydrocarbons have been used in the chemical process plants in various applications and temperature levels for more than 130 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".