Augmented Reality Indoor-Outdoor Navigation Through a Campus Digital Twin
Bibliographic record
Abstract
Geolocated services can play a significant role in enhancing user experiences, in complex environments like complex campuses, for onboarding new employees and students and guiding tourists and visitors. This motivation is driving research and development in the area of geolocated augmented reality (AR) navigation but the field is still quite young. CampusGo is a software platform designed to provide a coherent suite of services to enable the development of AR-enabled, smart-campus applications, including (i) integration of a variety of multimodal data about the campus, such as building architectural diagrams and 3D models of buildings, people's profiles, offices and labs, landmarks and events; (ii) indoor and outdoor path-planning services; (iii) real-time navigation support; and (iv) engaging web and mobile front ends. In this paper, we present the overall system architecture and describe our early experience with deploying and beta-testing CampusGo on a university campus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".