Using Web Augmented Reality to add Visual Interactions to Contactless Restaurant Menus in Response To COVID-19
Bibliographic record
Abstract
Businesses that are slow to adapt to the digital world risk getting left behind. This research project explores how restaurants can adopt emerging technologies such as web-based augmented reality (WebAR) to improve the customer’s ordering experience during the reopening phases of the economy from COVID-19 and beyond. Many restaurants that were permitted to reopen had to limit their menu offerings, adopted single-use paper menus, or asked customers to scan a QR code to view their digital menus. Standard menus are often engineered to influence customer purchases through clever content placement, visuals and other psychological tactics that increases a restaurant’s profitability. This project demonstrates how WebAR can be used in three different ways to enhance contactless menus to reestablish trust and build purchase confidence for customers. The overall goal was to illustrate how WebAR can reduce the barriers for restaurants to create augmented brand experiences through integration with their existing digital presence and the ease of access to these experiences for their target customers without the need to download apps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".