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Record W6931442429 · doi:10.5281/zenodo.7220198

CS2 LPA-D03-PROTECTED: Evaluation of a next generation oxygen system – assessment of usability, comfort and human performance

2022· report· en· W6931442429 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typereport
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsUsabilityCockpitInteroperabilityProcess (computing)CognitionHuman error

Abstract

fetched live from OpenAlex

The use of an oxygen system has many crucial aspects regarding usability, comfort, and its effects on human performance. To overcome shortcomings of common systems, a new oxygen mask was developed. It was aimed at enhancing the devices’ comfort and ease of use and at enabling the interoperability of the oxygen system within the cockpit environment. The project was a cooperation between the German Aerospace Center (DLR) and Safran Aerosystems (Clean Sky 2 Joint Undertaking framework). Our aim was to evaluate the new oxygen system with the parts oxygen mask and human-machine interface and to compare it to a customary system. Experiments using 20 pilots were conducted in two demonstrators providing the two oxygen systems. The procedure included normal cruise flight operation and emergency scenarios. Data were assessed by use of computerized questionnaires and cognitive performance tasks. Results showed that subjects were more satisfied with the usability and the comfort of the new mask at all three measurement time points. Cognitive performance with the new mask was not significantly impaired compared to the customary (legacy) mask. Differential effects and conclusions for the further development process are discussed.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.432
GPT teacher head0.424
Teacher spread0.008 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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