Bibliographic record
Abstract
Recent years have seen a huge growth in the demand for online virtual worlds. The type of these online systems can range from virtual meeting setups, to a more video game like competitive environment. An equally large number of virtual worlds have been developed to meet this demand, and the competition between these system is very strong. Developers of such systems can benefit from any edge they can get in terms of technical quality of the system or the enjoy ability of the online experience.We propose that a monitoring system designed especially for virtual worlds will be able to provide that `èdge" to the developers. As such, we present, in this Thesis, a flexible real-time monitoring architecture which caters to the specific challenges and requirements of virtual worlds. Handling huge amount of data present in the worlds is dealt by distributing the data gathering process between multiple node. The proposed system modifies the gathered data, into a form more suitable for users to observe in real-time, by filtering it before displaying the final result. We use Mammoth, a massively multiplayer research framework, as the test-bed for a sample implementation of the proposed architecture. We use the results of experiments conducted on this implementation to validate that the system is indeed suitable for real-time monitoring of virtual worlds.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".