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Record W4385421783 · doi:10.54254/2755-2721/8/20230272

The Exploration of AR on Intelligent Packaging

2023· article· en· W4385421783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied and Computational Engineering · 2023
Typearticle
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAugmented realityVariety (cybernetics)UsabilityEntertainmentHuman–computer interactionComputer scienceProduct (mathematics)MultimediaField (mathematics)Packaging and labelingEngineeringManufacturing engineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Augmented Reality is a technology that enhances the physical world by adding virtual elements to it, making our physical world more interactive and engaging. AR has been applied to a variety of industries, including education, healthcare, and entertainment. The field of intelligent packaging, on the other hand, is expanding and uses a variety of technologies, like sensors and QR codes, to add value to packaging by improving their usability, convenience, and safety. This essay introduced the use of AR on intelligent packaging. How AR enhance the user experience of a product. Compared the difference between AR + intelligent packaging and traditional packaging and explores the potential of augmented reality (AR) on intelligent packaging. Finally, the paper summarizes and looks forward to the full text.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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