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Record W2948728409 · doi:10.25105/ms.v10i1.4128

Pengaruh Perbedaan Tegangan Pemanas terhadap Performa Refrigeran R12, R134a dan MC134 pada Refrigeration Laboratory Unit

2019· article· en· W2948728409 on OpenAlexaboutno aff
Senoadi Senoadi, Arif Akhmad Aliandi, Rosyida Permatasari

Bibliographic record

VenueMESIN · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationAir conditioningEnvironmental scienceWaste managementEngineeringGas compressorMechanical engineering

Abstract

fetched live from OpenAlex

Along with technological developments and population growth, the need for air conditioner (AC) is increasing, both those used in industry, offices, buildings, housing, vehicles, and others. In the use of AC, refrigerant is needed. Based on the Montreal agreement, it was agreed to replace refrigerants that are more environmentally friendly. The refrigerant used today is refrigerant with CFC and HCFC compounds. The use of those refrigerants result in ODP and GWP. This study aims to determine the energy efficiency of three refrigerants: R12, R134a, and MC134, by comparing the COP value of a refrigeration laboratory unit machine. Based on the test results, COP of MC134 refrigerant has the highest value compared to R12 and R134a refrigerants. Therefore, it can be concluded that MC134, a hydrocarbon refrigerant, could be considered as a substitute for R12 and R134 refrigerants, eventhough flammability of the hydrocarbons at certain temperatures should be considered also.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.009

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

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