Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Networked games can provide groupware developers with important lessons in how to deal with real-world networking issues such as latency, limited bandwidth and packet loss. Games have similar demands and characteristics to groupware, but unlike the applications studied by academics, games have provided production-quality real-time interaction for many years. The techniques used by games have not traditionally been made public, but several game networking libraries have recently been released as open source, providing the opportunity to learn how games achieve network performance. We examined five game libraries to find networking techniques that could benefit groupware; this paper presents the concepts most valuable to groupware developers, including techniques to deal with limited bandwidth, reliability, and latency. Some of the techniques have been previously reported in the networking literature; therefore, the contribution of this paper is to survey which techniques have been shown to work, over several years, and then to link these techniques to quality requirements specific to groupware. By adopting these techniques, groupware designers can dramatically improve network performance on the real-world Internet.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it