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Record W2038586811 · doi:10.1177/1045389x14566522

Development and evaluation of hybrid seat cushions for helicopter aircrew vibration mitigation

2015· article· en· W2038586811 on OpenAlexaff
Viresh Wickramasinghe, D. G. Zimcik

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2015
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCushioningCushionEngineeringShakerVibrationStructural engineeringAircrewCar seatAutomotive engineeringAcousticsAeronautics

Abstract

fetched live from OpenAlex

This article investigates the use of novel energy-absorbing materials to reduce vibration transmitted through helicopter pilot seat cushions. A type of engineered material known as the hybrid air cushioning system was integrated into the design of helicopter seat cushions in addition to conventional foam materials. The prototype seat cushions were preliminarily evaluated as the bottom seat cushion on a Bell-412 nonarmored pilot seat through mechanical shaker tests. The promising cushion designs were further evaluated on a Bell-412 helicopter through extensive flight tests. The pilot whole-body vibration levels were assessed in accordance with ISO 2631-1:1997 standard. The flight test results demonstrated that the prototype cushions were able to reduce significantly the pilot whole-body vibration. Therefore, the integration of hybrid air cushioning system with Bell-412 seat cushion can serve as a low-cost solution to mitigate the vibration levels transmitted to helicopter pilots.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.065
GPT teacher head0.353
Teacher spread0.288 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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