Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies the effectiveness of TCP pacing in a data center setting. TCP senders inject bursts of packets into the network at the beginning of each round-trip time. These bursts stress the network queues which may cause loss, reduction in throughput and increased latency. Such undesirable effects become more pronounced in data center environments where traffic is bursty in nature and buffer sizes are small. TCP pacing is believed to reduce the burstiness of TCP traffic and to mitigate the impact of small buffering in routers. Unfortunately, current research literature has not always agreed on the overall benefits of pacing. In this paper, we present a model for the effectiveness of pacing. Our model demonstrates that for a given buffer size, as the number of concurrent flows are increased beyond a Point of Inflection (PoI), non-paced TCP outperforms paced TCP. We present a lower bound for the PoI and argue that increasing the number of concurrent flows beyond the PoI, increases inter-flow burstiness of paced packets and diminishes the effectiveness of pacing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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