Bibliographic record
Abstract
Network and system administrators need to analyse network traffic for maintenance, security, and planning purposes. The volume of data on modern networks, however, make such analysis extremely difficult using existing open source tools. In this paper we argue that administrators need tools that will allow them to be more aware of the state of their networks, and we describe our vision for tools that would support such ‘‘network awareness’ ’ by analysing and visualising packet aggregations that are defined by both packet headers and payloads. As a first step towards such tools, we have developed a library called qcap, a framework for packet and stream reconstruction that allows applications to tap packets at all layers of the network stack: from network, to transport, to the application layer. qcap is fast, able to process network data at speeds of 120 megabytes per second on commodity hardware; it is easy to use, providing a simple API that requires only a few lines of code to perform complex parsing tasks; and it is extensible, using BNF-like grammars to describe TCP protocols. We believe that qcap can provide the foundation for tools that will support greater network awareness for system administrators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".