Board 44: CampNav: A System for Inside Buildings and Campus Navigation
Bibliographic record
Abstract
Finding classrooms can often be a time-consuming task.To address this issue, we introduce CampNav, a comprehensive system featuring an Android mobile application displaying 3D visualizations of campus buildings' indoor floor maps.CampNav includes a comprehensive set of tools for automated data collection and processing.Allowing users to integrate new buildings maps into the application efficiently, reducing time and manpower.This application is built on Mapbox, a widely-used, semi-open source mapping API renowned for its lightweight and versatile mapping capabilities.We have enhanced its functionality to support 3D indoor display.A significant aspect of the system is the utilization and integration of CNNLoc, a neural network designed for Wi-Fi-based positioning.The initial testing of CampNav has received positive responses by students and faculty members, showcasing its user-friendly interface and effective navigational capabilities.The Surveys and the Net Promoter Score (NPS), indicates students' strong affinity for this software, with many expressing a willingness to recommend CampNav to their colleagues.The satisfaction rate in terms of time savings is 93% that emphasizes the importance of CampNav.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.024 |
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".