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Record W4365795838 · doi:10.1615/1-56700-052-5.510

THE TRANSITION OF CFCS AND HCFCS TO ALTERNATIVE REFRIGERANTS IN THE UNITED STATES

2023· article· en· W4365795838 on OpenAlexaboutno aff
Henry Hwong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantMontreal ProtocolAir conditioningRefrigerationEnvironmental scienceEngineeringOzone layerMeteorologyMechanical engineeringGas compressorGeography

Abstract

fetched live from OpenAlex

The United States is one of the largest markets in the world for air-conditioning and refrigeration equipment, and was one of the first countries to sign the Montreal Protocol and complies with its obligations through a U.S. national law called the U.S. Clean Air Act of 1990. Phaseout of CFCs in the U.S. is well underway and manufacturers, in compliance with the law, will meet or exceed the requirements set forth in the Montreal Protocol. HCFC-22 is the most widely used refrigerant in the U.S. This is due to the proliferation of residential and small commercial unitary air-conditioning equipment which relies almost exclusively on HCFC-22. It is obvious that the phaseout of the production of HCFC-22, currently scheduled for the year 2020, will have a significant impact on the United States. The HCFC-22 Alternative Refrigerants Evaluation Program (AREP) was established by the Air-Conditioning and Refrigeration Institute (ART) to assist manufacturers in obtaining performance data on a multitude of HCFC-22 and R-502 alternatives. Several alternatives are found to perform almost as well, and sometimes better than the baseline refrigerant. No single alternative emerged as an universal replacement for all applications.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.094

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.246
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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