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 ability to manage individual flows is a major benefit of Software-Defined Networking. The overheads of this fine-grained control, e.g. initial flow setup delay, can overcome the benefits, for example when we have many time-sensitive short flows. Coarse-grained control of groups of flows, on the other hand, can be very complex: each packet may match multiple rules, which requires conflict resolution. In this paper, we present ReWiFlow, a restricted class of OpenFlow wildcard rules (the fundamental way to control groups of flows in OpenFlow), which allows managing groups of flows with flexibility and without loss of performance. We demonstrate how ReWiFlow can be used to implement applications such as dynamic proactive routing. We also present a generalization of ReWiFlow, called Multi-ReWiFlow, and show how it can be used to efficiently represent access control rules collected from Stanford's backbone 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 | 0.000 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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