Bibliographic record
Abstract
Many of the community-area networks use commodity 802.11 hardware to form small wireless networks. Generally organized as a mesh, employing a single channel, and having a few gateways for wider-area access, they tend to offer poor bandwidth to end users. To increase bandwidth, the idea of leveraging multiple interfaces operating on different, non-overlapping, channels has been put forward recently. In this paper, we examine the performance of community wireless networks based on such multi-interface nodes. Our experiments demonstrate that the mere use of more dual-interface nodes does not necessarily create higher capacity. Indeed, in a number of cases we show that the throughput is lower than cases where fewer interfaces are used. We identify three causes for this throughput limitation: channel load, RTS/CTS and exposed nodes, and unfairness due to local traffic. Furthermore, we show that in random topologies, it is very often hard to achieve adequate throughput gain.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.027 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".