Wireless 3-hop Networks with Stealing Revisited: A Kernel Approach
Bibliographic record
Abstract
Wireless multi-hop networks are an important type of networks, which have been studied in the literature by several researchers. For example, in a recent study, Guillemin et al. (2013) obtained exact tail asymptotics for the two stationary marginal distributions for wireless 3-hop networks with stealing based on boundary value problems. Efficient algorithms and good approximations for system performance could be derived based on this asymptotic property. However, it has been pointed out (see Fayolle and Iasnogorodski, 2014) that the key boundary value problem proposed in Guillemin et al. (2013) is incorrect, and an erratum by the authors has been published, Guillemin et al. (2014), with further details to appear in the near future. In this paper, we revisit this wireless 3-hop network with stealing. Using a different approach, the kernel method, we obtain exact tail asymptotics not only for the marginal distributions, but also for the joint distributions, which matches exactly with the results in Guillemin et al. (2013) (and Guillemin et al., 2011). Based on this result, impact of stealing can be clearly revealed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".