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Record W2946774774

Performance and Analysis on Winter Air Conditioning Testing with Limited Pressure

2019· article· en· W2946774774 on OpenAlexaboutno aff
G. Mohankumar, Kumarasubramanian Ramar, K Sakthivel, P.Sarathkumar, M.Satheeshkumar

Bibliographic record

VenueInternational journal of advance research and innovative ideas in education · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationMontreal ProtocolAir conditioningEnvironmental scienceGlobal-warming potentialOzone depletionMoistureGlobal warmingWaste managementOzone layerProcess engineeringEnvironmental engineeringOzoneMeteorologyEngineeringGreenhouse gasClimate changeHeat exchangerGeographyMechanical engineeringEcology
DOInot available

Abstract

fetched live from OpenAlex

This project is related to the future phase-out of hydro chloro fluoro carbons (HCFCs) used in air conditioning system. Most commonly used refrigerant is R22. In field of refrigeration everyone trying to find alternative refrigerants for R-22. Because hydro chlorofluorocarbons (HCFCs) including R-22 is promised to be banned as per the Montreal protocol. Several refrigerants like R290, R407C, R410A, R134a are emerged as substitutes to replace R-22, the most widely used Fluoro carbon refrigerant in the world. It is become necessary to replace the R-22 by other refrigerants which are environmental friendly. In this project R-290 (hydrocarbon) is selected as alternative for R- 22. Because it has zero Ozone Depletion Potential and almost zero Global Warming Potential. The product is typically at least 97.5% pure with minimal level of critical impurities including moisture and unsaturated hydrocarbons. This make it ideal for use in all type of refrigeration systems.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueInternational journal of advance research and innovative ideas in educationSame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207