Erasure adversarial wiretap channels
Bibliographic record
Abstract
In an erasure adversarial wiretap channel (eAWTP-channel), the adversary can select a fraction ρr of the codeword to read, and a fraction ρe of the codeword to erase. The model can be seen as an extension of the wiretap II model where the adversary not only selects its view of the transmitted word, but also can erase a fraction of the codeword. eAWTP codes provide security and reliability for communication over eAWTP channels. We derive an upper bound on the rate of eAWTP codes, and give an efficient construction of a code family that achieves the bound, hence deriving secrecy capacity of the channel. We then show that the construction can also be used for AWTP channels in which instead of erasing code components, the adversary can add noise to the codeword. The construction is the only AWTP code with constant alphabet size.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".