Taxonomy-Based Routing Indices for Peer-to-Peer Networks.
Bibliographic record
Abstract
On the one hand, the lack of structure of Peer-to-Peer (P2P) networks is key to their robustness and accessibility. On the other hand, this same lack of structure creates difficulties in efficiently searching the contents of the network. This search problem must be addressed for P2P networks to grow beyond the world of file sharing. To this end, we present a novel approach for describing the documents accessible through peers as a taxonomy. We propose a scoring function which is used to route queries within the network based on such taxonomical information, as well as the number of results desired by the query. The scoring function aims to minimize the number of network messages required to answer a query. When comparing to a sequential query forwarding algorithm, our simulations have shown that our proposed technique is able to reduce the number of messages generated for a query by a factor of 10. Also, our experiments show that limiting the “time to live ” of a query is likely to make queries more expensive. 1
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".