Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to determine how suboptimal the Shannon code is, one should compare its performance with that of the optimal code, i.e., the corresponding Huffman code, in some sense. It is well known that in the worst case the redundancy of both the Shannon and Huffman codes can be arbitrarily close to 1. Beyond this worst case viewpoint, very little is known. In this paper, we compare the performance of these codes from an average point of view. The redundancy is considered as a random variable on the set of all sources with n symbols and its average is evaluated. It is shown that the average redundancy of the Shannon code is very close to 0.5 bits, whereas the average redundancy of the Huffman code is less than n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (1+ln n)+0.086 bits . It is also proven that the variance of the redundancy of the Shannon code tends to zero as n increases. Therefore, for sources with alphabet size n, the redundancy of the Shannon code is approximately 0.5 bits with probability approaching 1 as n→ ∞.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| 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 it