Creating Optimal Edit Metric Codes using a Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Implementing error correcting codes is a challenging problem in information theory and the search for optimal codes, those of maximum size, has been the focus of research for years. Edit distance is defined as the minimum number of substitutions, insertions, and deletions required to change one word into another. Edit metric codes can be used to detect and correct substitution, insertion, and deletion errors from noise that occurs during transmission or storage of data. Important applications include those related to DNA storage, in which all these types of errors can occur. A (n, M, d)<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> edit metric code consists of a set of M q-ary codewords of length n where all codewords are at edit distance at least d apart. Such a code is optimal if M has the largest possible value given n, d, and q. Using a steady state genetic algorithm, this work attempts to increase the largest known value or minimum bound of M for which there exists a binary edit distance metric code with fixed codeword lengths. This work compares the ability of variation operators (two crossover and two mutation) to produce the best known values of M for a set of parameters. The results show that most combinations of variation operators are able to match the best known results for the parameter sets used in this study. For n = 16, the combination of variation operators are able to increase the best known lower bounds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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