Continuous rainbow network flow: rainbow network flow with unbounded delay
Bibliographic record
Abstract
In Chapter 5, we introduced a practical approach to the NASCC problem by optimal diversity routing of MDC code streams (the RNF problem) and optimal design of the MDC codes by PET technique. There, we briefly discussed the role of the common rate of the descriptions r and the total number of possible descriptions K . The developments so far assumed communication with bounded delays, in which case the values of r, K become particularly important. When the delay constraint is relaxed, we find that the set of all achievable distortion tuples converges to a limit independent of the description rate r . This limiting region will turn out to have a simple representation by introduction of a new form of flow we call continuous Rainbow Network Flow, or co RNF, as opposed to the discrete version of the problem considered so far in the thesis. co RNF can be viewed as the generalization of the RNF to fractional flows and is the subject of Sections 7.1 and 7.2. In di RNF, we assumed the existence of K descriptions of equal rate r . co RNF, in one view, relaxes the constraint on description rates and allows for an arbitrary number of descriptions. Therefore, co RNF contains RNF as a special case. On the converse side, we show that the performance achievable with arbitrary description rates can be achieved, arbitrarily closely, using any description rate, provided that the delay constraint is relaxed and the number of descriptions is left unbounded.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".