Bibliographic record
Abstract
The Internet's evolution over the past 30 years (1971-2001), has been accompanied by the development of various network applications. These applications range from early text-based utilities such as file transfer and remote login to the more recent advent of the Web, electronic commerce, and multimedia streaming. For most users, the Internet is simply a connection to these applications. They are shielded from the details of how the Internet works, through the-information-hiding principles of the Internet protocol stack, which dictates how user-level data is transformed into network packets for transport across the network and put back together for delivery at the receiving application. For many networking researchers however, the protocols themselves are of interest. Using specialized network measurement hardware or software, these researchers collect information about network packet transmissions. With detailed packet-level measurements and some knowledge of the IP stack, they can use reverse engineering to gather significant information about both the application structure and user behavior, which can be applied to a variety of tasks like network troubleshooting, protocol debugging, workload characterization, and performance evaluation and improvement. Traffic measurement technologies have scaled up to provide insight into fundamental behavior properties of the Internet, its protocols, and its users. The author introduces the tools and methods for measuring Internet traffic and offers highlights from research results.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".