Bibliographic record
Abstract
The following topics are dealt with: information theory; lossy source coding; wiretap channel; wireless communication; caching; ad-hoc networks; network coding; polar coding; quantum channels; entropy; MIMO wiretap channel; message passing; wireless networks; index coding; quantum sensing; f-divergence; degrees of freedom; secrecy; communication theory; network delay; network stability; quantum protocols; hypothesis testing; multiterminal source coding; relay networks; locally repairable codes; Shannon theory; network security; sequential detection; energy harvesting; interference channels; quantum error control codes; Turbo codes; statistics; algebraic coding; multiple access channel; fading channels; channel capacity; optical communications; cellular systems; relay channels; signal processing; Gaussian channel; iterative decoding algorithms; lossless compression; information measures; distributed storage; lattice codes; dirty paper coding; data privacy; distributed information processing; dimensionality reduction; flash memories; compressed sensing; broadcast channels; space-time codes; relaying schemes; finite block length capacity; Reed-Solomon codes; cryptography; biometrics; modulation; demodulation; quantum communication; cognitive radio; bioinformatics; neuroscience; and wireless network protocols.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.782 | 0.671 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".