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Record W4391113495 · doi:10.60008/thequest.v2i2.95

Improvised Freon Extractor as an Innovative Trainer

2023· article· en· W4391113495 on OpenAlexaboutno aff
Tranquilino J. Lucas

Bibliographic record

VenueThe QUEST Journal of Multidisciplinary Research and Development · 2023
Typearticle
Languageen
FieldEngineering
TopicEngineering and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsAir conditioningFreonRefrigerationHVACMontreal ProtocolRefrigerantEnvironmental scienceWaste managementEngineeringAutomotive engineeringGas compressorOzone layerProcess engineeringMechanical engineeringMeteorologyChemistryOzone

Abstract

fetched live from OpenAlex

The decade of the 1990's has been a challenging time for the Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) industry worldwide. Due to its vital role in the destruction of the stratospheric ozone layer, provisions of the Montreal Protocol and its various amendments required the complete phase-out of chlorine-containing refrigerant such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs). Appliances such as window type air-conditioners, motor vehicle air-conditioners, and refrigerators, rely on ozone depleting refrigerants and their substitutes. In this case, the regulation of the Environmental Protection Agency (EPA) which prohibits the venting of refrigerants compounds to the atmosphere must be followed. Along this line of concern, educational institutions need to be aware of this technological gap specifically in developing programs in Heating, Ventilation, Air-conditioning and Refrigeration for environmental protection against incorrect disposal of refrigerant. The aim of this study was to develop an Improvised Freon Extractor as an Innovative Trainer to help students and instructors in the Bachelor of Industrial Technology and
 Bachelor of Science in Mechanical Engineering courses in their effective teaching-learning of lessons in Refrigeration and Air-conditioning subjects. The results of the study revealed that the developed innovative trainer contains the following parts: electrical parts - includes the dual capacitor, banana plug, compressor, fan motor, air swing motor, air swing motor switch, thermostat switch, and selector switch; nonelectrical parts - frame housing, frame cover, suction and discharge valve, caster wheels, filter receiver drier assembly and condenser. The Improvised Freon Extractor as an Innovative Trainer was also found to be suitable to serve as an alternative learning material that assists the learning of Refrigeration and Air-conditioning subjects in the Bachelor of Industrial Technology and Bachelor of Science in Mechanical Engineering courses

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.075
GPT teacher head0.367
Teacher spread0.292 · 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
Published2023
Admission routes1
Has abstractyes

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