Properties of guided scrambling encoders and their coded sequences
Bibliographic record
Abstract
Guided scrambling (GS) line codes augment the source bit stream prior to self-synchronizing scrambling to ensure that the scrambling process generates an encoded bit sequence with good line code characteristics. With arithmetic from the ring of polynomials over GF(2), self-synchronizing scrambling can be interpreted as division of the source bit sequence by the scrambling polynomial and transmission of the resulting quotient. When augmenting bits are inserted in fixed, periodic positions, GS codes can be interpreted as block line codes which encode source words to quotients. In particular, block guided scrambling (BGS) generates a transmitted bit stream which is a concatenation of finite-length quotients chosen from sets of quotients which represent each source word. Alternatively, in continuous guided scrambling (CGS), the transmitted sequence appears to be a continuous quotient due to the fact that the encoder shift registers are updated following quotient selection to contain the remainder associated with the selected quotient. The quotient selection mechanisms of both BGS and CGS encoders can be modeled as finite state machines with quotient sets as input and the selected quotient as output. In CGS encoding, the selection mechanism also outputs the remainder associated with the selected quotient. In this paper we describe several characteristics of GS encoders and their coded sequences.
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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".