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Record W7056255437

The environmental comfort experience and activities of flight attendants in a turboprop airplane

2023· article· en· W7056255437 on OpenAlexaff

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2023
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsTurbopropAirplaneWorkloadWork (physics)AviationAircraft noiseNoise (video)Propeller
DOInot available

Abstract

fetched live from OpenAlex

The aviation industry needs to reduce CO2 emissions. Turboprop aircrafts consume 10-60% less fuel compared to regional jets. In addition, electric propeller aircrafts are now in development, which can be CO2 neutral. However, in turboprop aircrafts the noise level is high and the space is limited. For flight attendants that work long hours in these aircrafts, this could become demanding. In this paper, the environmental comfort and ergonomics are studied in an experiment in a turboprop aircraft as a base for improving the working conditions for cabin personnel in future propeller aircrafts.<br/>In general, it can be concluded that the tasks of the FAs in a turboprop are challenging regarding both physical and mental aspects. Unfavourable postures, high forces required for manoeuvring the trolley, little recovery time and a noisy environment all contribute to increased physical workload levels, which cause discomfort. The work is mentally demanding as resting time is very limited on short flights. When developing aircraft interiors, attention should be paid to reduce cabin noise and to ergonomic designs that require lower physical forces and allow FAs to work with healthy postures.<br/>

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.262

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 studies0.0000.001
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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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 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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