Error detection algorithm for Lempel-Ziv-77 compressed data
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we develop a novel error detection algorithm for Lempel-Ziv-77 (LZ77) compressed data. In the proposed algorithm, additional bits are not used to detect bit errors, unlike in conventional methods such as checksum, cyclic redundancy check, Hamming code, and repetition code. We also introduce eight special features of LZ77-compressed data for detecting the presence of errors. We demonstrate the feasibility of the algorithm based on simulations and evaluate it using two publicly available databases comprising the Calgary and Canterbury corpora. The error detection rate using the proposed algorithm is below those of conventional methods, but the compression ratio is better. The application of a parity bit in the algorithm improves the error detection performance. The number of redundant bits increases owing to the insertion of the parity bit, but the code rate is still greater than or equal to 0.9, whereas conventional methods obtain code rates less than 0.9. Simulations demonstrate that the algorithm obtains significant performance improvements when a parity bit is periodically inserted. In particular, we achieve an error detection rate of 100% using the parity bit when the number of bit errors is greater than seven.
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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 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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