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European experience application in replacing hydrofluorocarbons in air conditioning units in domestic railway transport

2021· article· en· W3169443102 on OpenAlexaboutno aff
И. М. Мазурин, С. Н. Науменко

Bibliographic record

VenueRussian Railway Science Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicTransportation Systems and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantFreonMontreal ProtocolEuropean unionChlorofluorocarbonAir conditioningOzone layerTask (project management)Environmental scienceEngineeringBusinessInternational tradeMeteorologyGeographyChemistryMechanical engineeringOzone

Abstract

fetched live from OpenAlex

Adoption by Russia of the Kigali Amendment to the Montreal Protocol on substances that deplete the ozone layer posed a very difficult task for consumers of artificial cold, including rail transport, to find an acceptable alternative to the R134a freon and mixtures based on it, which is being phased out. Considering that there are no equivalent alternatives to these substances on the market, it was proposed to consider the use of widely known and previously used working fluids in climate technology, based on the positive experience of the European Union (EU) countries. The article analyzes the reasons for the bans on the use of hydrofluorocarbons, presents the mechanism for the legal use of refrigerants that are safe for humans and nature in the EU and a version of the roadmap for converting the climatic units of the Russian Railways holding to R22 freon and other types of fluorocarbons within the framework of the Kigali Amendment.

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)0.005
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.244
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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