SORTING SIGNED PERMUTATIONS BY FIXED-LENGTH REVERSALS
Bibliographic record
Abstract
A signed n-permutation is a permutation on {1,2,…,n} in which each element is labelled by a positive or negative sign. Here we consider the problem of sorting signed permutations by fixed-length reversals. Indeed, limiting the transformations to reversals of length exactly k can be very restrictive, for example, (+1,+3,+2,+4,…,+n) can never be sorted to (+1,+2,+3,+4,…,+n) by 2-reversals. That is, for given two signed permutations it is not obvious whether they can be sorted to each other by k-reversals. Thus in 1996, Chen and Skiena gave the following open problem: what is the connectedness of signed permutations under fixed-length reversals? In this paper, we resolve this open problem when "fixed-length" is even, and give a characterization of the connectedness of signed n-permutations under 2l-reversal, for both linear and circular permutations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".