A recursive method for bias estimation in asymmetric packet-based networks
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Bibliographic record
Abstract
Clock synchronization in many protocols such as IEEE 1588 is achieved by exchanging timing information between a master and slave node. Packet delay variation (PDV) is a major source of inaccuracy in packet-based synchronization systems. When the expected values of the delays from master to slave and from slave to master are not equal, the synchronization problem can be modeled as a biased estimation problem. In this paper we propose a solution to estimate the delay bias and use this estimate to improve the synchronization accuracy. Our method is easy to implement and is compatible with the current version of the protocol. Moreover, this method allows us to update the slave clock recursively rather than after collecting many samples. The proposed method works well in the presence of frequency offset and does not require any assumption on the filter which is used in the synchronization process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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