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
Irregular low-density parity-check (LDPC) code design for parallel sub-channels with different qualities is investigated. Such channels appear in many communication systems, e.g., orthogonal frequency-division multiplexing systems. When channel knowledge is available at both the transmitter and receiver, following the literature, we consider allotted LDPC codes which carefully assign different parts of the code to sub-channels. To reduce the number of design parameters and allow for efficient design, semi-regular allotted codes have been suggested. We first formulate the design of semi-regular codes as a mixed integer linear programming. Relaxing the semi-regularity constraint broadens the search space which results in improved codes and also a more efficient design via linear programming. While information theoretic results suggest that having channel state information-for a fixed power assignment-does not change the capacity, we show that under non-optimal decoding or when the maximum degree allowed in the code is small, allotted codes significantly outperform conventional ones. Finally, for the case that neither side has the channel knowledge (thus capacity-loss is inevitable), we see that the reduced capacity can still be approached by LDPC codes.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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