Rigorous evaluation of performance and policy impacts of transport protocols and in-network devices
Bibliographic record
Abstract
The popularity of resource-hungry applications is at record high and is increasing, creating a data consumption boom that is growing at a rapid rate. As a result, resource-constrained networks need to keep up with this demand for data and bandwidth, while guaranteeing accessibility, reliability, and speed to maintain a satisfactory level of end-to-end performance for all users and applications. To achieve this goal, much effort has been put into optimizing for network performance, including optimizing Web applications to adapt to network conditions, designing new transport protocols that better fit modern applications requirements, and applying in-network management techniques by network operators for better handling of traffic loads. However, many of aforementioned solutions do not go through sufficient evaluation, resulting in poor understanding of their implications or how well they interact with other optimization efforts. This can cause gaps between intended and actual performance of applications across a range of environments. Moreover, some of these approaches disrupt the Internets openness and neutrality. Further frustrating such scenarios is the lack of visibility into networks, making it very difficult (or impossible) to pinpoint the root causes of poor performance or detect open Internet violations.
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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 | 0.001 | 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".