MONI- <i>k</i> : An index for efficient pangenome-to-pangenome comparison
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Maximal exact matches (MEMs) are widely used in bioinformatics, originally for genome-to-genome comparison but especially for DNA alignment ever since Li (2013) presented BWA-MEM. Building on work by Bannai, Gagie and I (2018) and again targeting alignment, Rossi et al. (2022) recently built an index called MONI that is based on the run-length compressed Burrows-Wheeler Transform and can find MEMs efficiently with respect to pangenomes. In this paper we define k -MEMs to be maximal substrings of a pattern that each occur exactly at least k times in a text (so a MEM is a 1-MEM) and briefly explain why computing k -MEMs could be useful for pangenome-to-pangenome comparison. We then show that, when k is given at construction time, MONI can easily be extended to find k -MEMs efficiently as well.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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