Bibliographic record
Abstract
We review the principles of computer performance modelling with queues, and validate the models using the empirical data. We focus on modelling a special configuration of two computers working in tandem: a mirroring system. Distributed system under investigation has implemented as a series of Java applets communicating using the functions of java net.* package. The mirroring aspect of the system is modelled analytically with a difference queue. The difference queue is defined for a network of two queues in parallel, as the queue consisting of elements present in one queue and absent from the other queue. The queueing models under investigation are M/M/1, and M/D/1 queues. In our study we have observed that the service times were not completely random on a typical server and could not be approximated well by exponential distribution. Our results have shown that the service was almost (i.e. with a very small variance) deterministic. We have also found that the d-queue model approximates well the behaviour of the mirroring system under low load conditions, and also provides an upper bound on the delays under high load conditions.
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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 | 0.012 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".