Identifying and Understanding Scientific Network Flows
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
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 the status of the work carried out to tackle this challenge by the Research Technical Networking Working Group (RNTWG) and 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 early 2024. The Scitags initiative is a long-term effort to improve the visibility and management of network traffic for data-intensive sciences. The efforts of the RNTWG and Scitags initiatives have 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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it