Analysis of queueing-time distributions for MAP/D<sub><i>N</i></sub>/1 queue
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Bibliographic record
Abstract
With the advent of highly integrated transmission networks, the transmission of data, such as audio, video, video streaming are transmitted over a common medium in smaller file sizes. These files have variable transmission time requirement which is of deterministic in nature. Further, the traffic generated from various sources is bursty and correlated. This particular scenario can be best modelled as a single-server queueing system with arrivals following a Markovian arrival process and service-time deterministic distribution taking one of the N possible values. For this model, we present a closed-form (in terms of roots) analysis for evaluating queueing/system-time distributions using an inversion method which is based on the roots of associated characteristic equation. Several numerical examples are presented with complete discussion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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