Bibliographic record
Abstract
Consider the problem of broadcasting an i.i.d. source sequence X = {X/sub i/} /sub i=1//sup N/ (possibly N /spl rarr/ /spl infin/) to n listeners over a discrete broadcast channel, consisting of n channels with capacities C/sub 1/ = C/sub max/ /spl ges/ C/sub 2/ /spl ges/.../spl ges/ C/sub n/ = C/sub min/. Let the tuple D = (D/sub 1/, D/sub 2/,...,D/sub n/) represent the average distortion in reconstructing sources at the n listeners. The problem of characterizing all achievable tuples D is still open for a general case. For a fairly general class of discrete channels, we prove the achievability of the tuple n(/spl rho//sub 1/,/spl rho//sub 2/,...,/spl rho//sub n/) = (D/sub X/(/spl rho//sub 1/C/sub 1/ /spl zeta/), D/sub X/(/spl rho//sub 2/C/sub 2/ - /spl zeta/),...,D/sub X/(/spl rho//sub n/C/sub n/ - /spl zeta/)), provided that /spl lambda//sub i/ = (/spl rho//sub i/C/sub i/ - /spl rho//sub i/+/sub 1/C/sub i+1/)/C/sub i/ > 0, for 1 /spl les/ i /spl les/ n $1, /spl lambda//sub n/ = /spl rho//sub n/ and /spl Sigma//sub i=1//sup n-1/ /spl lambda//sub i/ /spl les/ 1, where D/sub X/ (R) is the distortion rate function of X. The penalty term /spl zeta/ = 1/2 for a general source with real alphabets and is /spl zeta/ = 0 if X is progressively refinable. The factor 00, we find examples of channels for which /sup 3/(2/3+/spl delta/,2/3+/spl delta/,2/3+ /spl delta/) is not achievable.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".