A Hybrid Quality-of-Experience Taxonomy for Mixed Reality IoT (XRI) Systems
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.
Bibliographic record
Abstract
Mixed Reality (XR) and the Internet-of-Things (IoT) are two rapidly advancing paradigms, gaining maturity toward the near term, in both industry, government and other organizations, and consumer scenarios. These domains are converging simultaneously, leading to XR systems with IoT embedded capabilities in smart environments, and IoT systems with more immersive, engaging, and adaptive interfaces and use cases. Synergies between these system design platforms are currently being explored, although there remains the need for a clear treatment of the human-factor and quality of experience perspectives of these hybrid XR and IoT (XRI) systems. This work contributes a new taxonomy derived from synthesis of usability literature and other design considerations within these disciplines toward a framework for XRI system design, development, and evaluation. It is hoped that this enables future researchers and developers of XRI systems to create more impactful, functional, and usable XRI across multiple domains in the near future.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it