High-performance telepointers
Bibliographic record
Abstract
Although telepointers are valuable for supporting real-time collaboration, they are rarely seen in commercial groupware applications that run on the Internet. One reason for their absence is that current telepointer implementations perform poorly on real-world networks with varying traffic, congestion, and loss. In this paper, we report on a new implementation of telepointers (HPT) that is designed to provide smooth, timely, and accurate telepointers in real-world groupware: on busy networks, on cable and dialup connections, and on wireless channels. HPT maintains performance at usable levels with a combination of techniques from multimedia and distributed systems research, including UDP transport, message compression, motion prediction, adaptive rate control, and adaptive forward error correction. Although these techniques have been seen before, they have never been combined and tailored to the specific requirements of telepointers. Tests of the new implementation show that HPT provides good performance in a number of network situations where other implementations do not work at all - we can provide usable telepointers even over a lossy 28K modem connection. HPT sets a new standard for telepointers, and allows designers to greatly improve the support that groupware provides for real-time interaction over distance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".