Bibliographic record
Abstract
Packet drops have a great impact in the RTT variation and the throughput experienced by some TCP applications. However, using packet drops is the primary way TCP uses to discover the bandwidth available in order to proceed with its packet transmissions. A different approach is used by SmoothTCP-q, where ICMP-SQ messages are sent to the SmoothTCP-q sender every time a threshold is reached in the queue size of the router. In this mechanism, SmoothTCP-q tries to avoid packet drops since it is not necessary anymore to overload the router queue to discover the network bandwidth. Consequently, it is important to have some way to determine the queue threshold and evaluate its effect on SmoothTCP-q. This paper briefly describes SmoothTCP-q and presents a simple model for this relationship that can be used to evaluate whether or not there are packet drops in a SmoothTCP-q connection
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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".