Bibliographic record
Abstract
The development of a communications infrastructure for the Shared Reality Environment, a high-end immersive, telepresence space, is described. Such a system must support low-latency, high fidelity, bi-directional audio and video transfer between a number of locations interconnected by an IP network. As a first attempt, MJPEG video, accompanied by monaural audio was transmitted between two laboratory spaces in the McGill Centre for Intelligent Machines. The relatively high latency resulting from JPEG compression and decompression motivated a hybrid approach in which raw data was selectively transmitted whenever the overhead in doing so was less than the cost of JPEG processing. Next, to scale up to the demands of transmitting multi-channel audio over greater distances, the protocol was refined and demonstrated by streaming, over the Internet, a live concert from McGill's Redpath Hall to an audience at New York University. This is believed to be the first-ever demonstration of this nature. While these experiments have proven successful within the limited context of their test environment, some challenges remain to be addressed in order for the system to support the full demands of a Shared Reality immersive telepresence application.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.032 | 0.016 |
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".