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Record W4390476718 · doi:10.24908/agt.v1i3.17179

The Dyson Zone: A Groundbreaking Audio and Air-purifying Technology

2023· article· en· W4390476718 on OpenAlexaff
Yash Raj Shrestha, Livia Muir, Analidhia De Almeida, C Bushell

Bibliographic record

VenueAging and (Geron) Technology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsIndigenousIsolation (microbiology)PollutionDual (grammatical number)Air quality indexAir pollutionSocial isolationNoise (video)Work (physics)Computer scienceFilter (signal processing)Environmental scienceEnvironmental planningBusinessPsychologyMeteorologyGeographyEngineeringEcologyArtificial intelligenceBiologyPsychotherapist

Abstract

fetched live from OpenAlex

Outdoor activities in both rural and urban areas are often hindered by air pollution. The brand-new Dyson Zone is a technology allowing individuals to concurrently enjoy noise-cancelling music and get high-quality protection from air pollution. In research trials, Dyson’s dual-filter system has proven robust efficacy in preventing the inhalation of polluted air. The availability of this product is affected by support networks, the presence of disabilities, and Indigenous status. These social determinants of health work both in isolation and in combination to provide preferential accessibility to certain subgroups. As the Dyson Zone provides a novel introduction to future purification systems, further research should be reinforced.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.363
Teacher spread0.340 · 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.

Study designNot applicable
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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