An Empirical Evaluation of the Student-Net Delay Tolerant Network
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Bibliographic record
Abstract
Radio equipped mobile devices have enjoyed tremendous growth in the past few years. We observe that in the near future it might be possible to build a network that routes delay-tolerant packets by harnessing user mobility and the pervasive availability of wireless devices. Such a delay-tolerant network could be used to supplement wireless infrastructure or provide service where none is available. Since mobile devices in a delay-tolerant network forward packets to nearby users, the devices can use short-range radio, which potentially reduces device power consumption and radio contention. The design of a user mobility based delay-tolerant network raises two key challenges: determining; the connectivity of such a network, and determining the latency characteristics and replication requirements of routing algorithms in such a network. To determine realistic contact patterns, we collected user mobility data by conducting two user studies. We outfitted groups of students with instrumented wireless-enabled PDAs that logged pairwise contacts between study participants over a period of several weeks. Experiments conducted on these traces show that it is possible to form a delay-tolerant network based on human mobility. The network has good connectivity, so that routes exist between almost all study participants via some multi-hop path. Moreover, it is possible to effectively route packets with modest replication
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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