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Record W4362466512 · doi:10.1088/1361-6439/acc955

Semi-reflective visor-based compact head-worn light field display

2023· article· en· W4362466512 on OpenAlexafffund
Hongbae Park, Boris Stoeber

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrolensComputer graphics (images)Computer scienceViewing angleOpticsLiquid crystal on siliconPlane (geometry)Computer visionOptical head-mounted displayLine (geometry)Field of viewHead (geology)Artificial intelligenceLens (geology)PhysicsLiquid-crystal displayGeometryGeologyMathematics

Abstract

fetched live from OpenAlex

Abstract We propose a visor-based see-through head-worn light field display. The visor is a semi-reflective concave image combiner that overlays virtual imagery to the user’s visual field. The visor has a toroidal surface profile for off-axis astigmatism correction. Virtual images are created at different depths using a classical light field assembly (LFA), which comprises a microlens array and a display source. The LFA is placed at an angle above the visor, clear of the user’s line of sight. The image plane of the LFA is placed near the focal plane of the visor. Since the LFA forms virtual images farther away from the plane of the display source, the LFA itself can be brought close to the visor, allowing for a more compact display system compared to conventional head-worn displays.

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)0.000
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.243
Teacher spread0.235 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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