Scitags: A Standardized Framework for Traffic Identification and Network Visibility in Data-Intensive Research Infrastructures
Bibliographic record
Abstract
The High-Energy Physics (HEP) and Worldwide LHC Computing Grid (WLCG) communities have faced significant challenges in understanding their global network flows across the world’s research and education (R&E) networks. This article describes an update on the status of the work carried out to tackle this challenge by the Scientific Network Tags (Scitags) initiative, including the evolving framework and tools, as well as our plans to improve network visibility before the next WLCG Network Data Challenge in 2026. The Scitags initiative is a long-term effort to improve the visibility and management of network traffic for data-intensive sciences. It has created a set of tools, standards, and proof-of-concept demonstrators that show the feasibility of identifying the owner (community) and purpose (activity) of network traffic anywhere in the network.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".