Bibliographic record
Abstract
We propose a network information flow strategy of optimal multicast with erasure correction coding (OMEC), in which erasure correction is a means of utilizing diversities rather than combating packet losses. An information source is first encoded by an erasure correction code and then optimally multicast by a server to sinks. The OMEC scheme has the following characteristics: (1) It is at least as good as optimal routing and, for some cases, can realize a network throughput arbitrarily larger than that achievable by pure routing. (2) For most scenarios, the approach yields a throughput comparable with the optimal throughput achievable with network coding. In fact, for most examples in the literature for which network coding makes high throughput gains compared to pure routing, OMEC performs close to the optimal. (3) Unlike general network coding, intermediate nodes are only required to route information. This makes OMEC compatible with currently deployed network structures. (4) Surprisingly, unlike pure optimal routing, OMEC can be efficiently computed in polynomial time. While OMEC is, in general, suboptimal compared to full fledged network coding, it enjoys most of the computational and throughput benefits associated with network coding over pure routing, without requiring any changes to existing hardware infrastructures or protocol stacks
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".