Performance and Safety Analysis of Standardized Refrigeration Topologies Using WSM Method
Bibliographic record
Abstract
After the discontinuation of ozone-depleting substances, various pieces of equipment such as air conditioners, water chillers, and medium-temperature commercial and home refrigeration systems used the refrigerant medium-pressure hydrofluorocarbon (HFC) R-134a. However, R-134a has a Global Warming Potential (GWP) of 1300, meaning it has a detrimental influence on the environment. It is now regulated by the Kigali Amendment to the Montreal Protocol (UNEP, 2016). Since the invention of the earliest vapor compression refrigeration system by Jacob Parkin in 1834, numerous chemical compounds have been tested as refrigerants. Ammonia, carbon monoxide, sulfur oxide, and methyl chloride were the main refrigerants used in the 1930s. Benzyl sodium chloride, propane, iso-butane, and freshwater were utilized to a lesser extent. However, the class of chemicals that includes chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) became the predominant types of refrigerants after the development of dichlorofluoromethane compounds in 1930 by Thomas Middleton and Albert Henna. The weighted sum technique is a decision-making process that considers numerous possibilities and factors before choosing the best option. A weighted collection of sums, or weighted mean voting ensemble, is a machine learning strategy that combines predictions from various models, with each model's contribution being weighed according to its capacity or level of expertise. The benefits of using this method include ease of use, especially when dealing with complex problems, and the ability to assign weights in a straightforward manner, even when finding solutions and goals becomes challenging within an all-in solution space. Critical Temperature, Critical Pressure, Saturated Pressure, and Liquid Density are important parameters for refrigerants such as R134a, R152a, R1234yf, R1234ze (E), and R1233zd (E). The results show that R1233zd (E) obtained the highest rank, while R1234yf received the lowest rank. According to the dataset, the Weighted Sum Method (WSM) indicates that R1233zd (E) is the best refrigerant, considering breathing rate and its top ranking.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".