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Record W2947876972 · doi:10.1080/14606925.2019.1594977

Considering Haptic Feedback Systems for A Livable Space Suit

2019· article· en· W2947876972 on OpenAlexaff
Torstein Hågård Bakke

Bibliographic record

VenueThe Design Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicSpace Exploration and Technology
Canadian institutionsKwantlen Polytechnic University
FundersNational Institute for Occupational Safety and HealthMarshall Space Flight Center
KeywordsHaptic technologySpace (punctuation)Human–computer interactionComputer scienceEngineeringSimulation

Abstract

fetched live from OpenAlex

The paper explores protective equipment for work in extreme environments manifested in a proposal for a haptic feedback system for astronauts.It follows the thesis that the safety of astronauts wearing Extra-Vehicular Activity (EVA) suits, whether in space or on planetary surfaces, is connected to their ability to interact with their environments, their equipment and suits, and their coworkers.The project emphasises the use of new technologies to enhance the quality of said interactions.Focusing on manned exploration and construction activity in space, qualitative research methods are employed to gain an overview of the factors that dictate work in space, endeavours in design and architecture for space, and research into the ways humans interact with their surroundings.Lastly, a conceptual prototype was made to explore the possibilities of exploring a language of haptic feedback to complement other systems and to mediate the sensory filters imposed by the modern space suit.

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)0.002
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.034
GPT teacher head0.215
Teacher spread0.181 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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