Using Web Augmented Reality to add Visual Interactions to Contactless Restaurant Menus in Response To COVID-19
Bibliographic record
Abstract
<p>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. </p>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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 teacher head, 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".