On the spectral characteristics of continuous guided scrambling line codes
Bibliographic record
Abstract
Line codes are used in digital communication systems to control the characteristics of transmitted symbol sequences. Recently, an efficient and easily implemented family of codes called guided scrambling (GS) codes has been introduced. Previous work indicated that continuous GS (CGS) codes can be configured to generate encoded sequences with statistics nearly independent from those of the source bit stream, a property not available with any other efficient line code technique developed to date. This paper presents analysis of the spectral characteristics of these codes which confirms this conjecture. The principle of guided scrambling is reviewed. Then, by showing that the scrambling polynomials currently recommended for GS codes can be regarded as base polynomials for large families of polynomials, expanded sets of scrambling polynomials are proposed. Evaluation of the power spectral density of CGS codes which use scrambling polynomials from these expanded sets is then considered, under the condition only that the input bit stream is stationary. Expressions which limit required computation to practical limits are developed, and results for several code configurations are given. Here it is demonstrated that when scrambling polynomials of high degree are used, the characteristics of the transmitted symbol sequence become nearly independent of the source bit stream statistics.>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".