Lossless multicast with a single source
Bibliographic record
Abstract
We start by perhaps the simplest variation of the NASCC problem, namely that of lossless information multicast. This problem has been the source of much interest and studies in the past several years, as ignited by the original work on network coding [8]. We are not going to review the large and exciting body of work on lossless multicast in this book in great detail. However, we will review some of the basic concepts in order to put this book in a proper historical and comparative context. We refer the reader to an array of excellent new books on network coding for further reading [57, 58]. Network coding, the multicast scenario In the notations of this book, the network information flow problem introduced by Ahlswede et al . in [8] can be defined, for the case of a single information source, with the following elements. A directed graph G 〈 V, E 〉 with node set V and edge set E ⊂ V × V . A function R : E → ℝ + that assigns a capacity R(e) to each link e ∈ E . An information source I that generates information at a server node s ∈ S at a rate h bits per time unit. A set of sink nodes T ⊆ V that are interested in receiving this information. The multicast demand h is said to be admissible with capacity constraint function R , or equivalently ( G, S, T, R, h ) is said to be admissible if there exists a coding scheme that satisfies the multicast requirement rate h and respects the capacity constraints on all links e ∈ E .
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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 | 0.000 | 0.001 |
| 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 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".