Efficient estimation techniques and a new description of variable-bit-rate video traffic parameters
Bibliographic record
Abstract
Traffic descriptors (TDs) for ATM connections are mandatory for many network operations such as call acceptance and network resources allocation. For variable bit rate (VBR) services, the TDs are defined by the ATM Forum as peak cell rate, sustainable cell rate, and maximum burst size. However, the accurate characterization of VBR source traffic in terms of these descriptors is still an open issue. We use the leaky bucket algorithm to estimate the TDs for various MPEG-1 video sequences. We study the effect of two different schemes, namely, delaying and peak removal on the TD values. We show that the valves of TDs are reduced while introducing delay and loss to the video traffic. We show that a single set of TDs does not accurately characterize the MPEG video traffic. Therefore, we propose a new approach for VBR traffic description which involves a sequence of TDs that describes the traffic more accurately while introducing no loss or delay.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".