Bibliographic record
Abstract
In this paper we propose a fully distributed peer-to-peer (P2P) infrastructure supporting Networked Virtual Environment (NVE) applications, such as massively multiplayer online games (MMOG). While many attempts have been made to tackle one of the most challenging issues in MMOGs - interest management, none of them are considered truly successful. Our architecture is a hybrid scheme focusing on NVEs' interest management. Our scheme takes the advantage of both structured overlay, i.e. Distributed Hash Table (DHT), and the unstructured P2P architecture. It not only has more stable and consistent performance with respect to neighbor discovery, but also is more scalable and fault tolerant than the existing approaches. Unlike other hexagonal zoning approaches in which each participant has a discrete view of the virtual world, our zoning design guarantees that all participants have a continuous view. Moreover, our novel hierarchical architecture and the message dissemination algorithm greatly save network bandwidth and alleviate each node's workload. We implemented our infrastructure running on top of an emulated network. We also implemented a simple game simulation and a visualization tool to demonstrate and visualize our infrastructure.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.222 | 0.133 |
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".