Computation of the residual packet loss probability in a binary multicast tree
Bibliographic record
Abstract
In order to achieve a better quality of service (QoS), the use of reliable multicasting has become increasingly important especially with the emergence of Internet-based applications such IP telephony, audio/video conferencing. In this paper, the residual packet loss probability in a complete binary multicast tree which consists of N routers with a given probability of successful delivery to the next router is evaluated, when automatic repeat request (ARQ) multicast repairs is employed. In this paper, we also derive and compare two other mathematical expressions, which can be used to calculate the final packet loss probability in a binary tree where reliable ARQ multicasting is used. These expressions can be used in the case of IP or MPLS multicasting. The first expression, which is called the average packet loss probability for ARQ deals only with the number of routers that should have correct transmissions (no loss and no errors) during the repair trial of one previous loss depending on the location of this previous loss. The second one, which is called the accurate expression, takes into account the number of trials, the number of errors and the position of each error (at which level the error occurred).
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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".