IEEE Telepresence Roadmap: Current Status and Call for Participation
Bibliographic record
Abstract
Telepresence technologies can transport one’s sense, skills and presence to any place in an instant. These technologies are experiencing numerous advances and breakthroughs and made meaningful impacts upon education, healthcare, safety, and beyond. The IEEE Future Directions Telepresence Initiative was launched in 2021 with the aim to catalyze and streamline these developments; it is currently preparing a telepresence roadmap to help guide the research community and communicate the challenges to stakeholders in industry and government. This paper shares motivating example applications and presents the current framework of the roadmap. This framework integrates three pillars or points of view: the task perspective, the system perspective, and the human perspective. We seek active participation of telepresence experts in the workshop to ensure a complete and inclusive roadmap.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".