Genome Halving and Double Distance with Losses
Why this work is in the frame
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Bibliographic record
Abstract
Given a phylogenetic tree involving whole genome duplication events, we contribute to solving the problem of computing the rearrangement and double cut-and-join (DCJ) distances on a branch of the tree linking a duplication node d to a speciation node or a leaf s. In the case of a genome G at s containing exactly two copies of each gene, the genome halving problem is to find a perfectly duplicated genome D at d minimizing the rearrangement distance with G. We generalize the existing exact linear-time algorithm for genome halving to the case of a genome G with missing gene copies. In the case of a known ancestral duplicated genome D, we develop a greedy approach for computing the distance between G and D, called the double distance. Two algorithms are developed in both cases of a genome G containing exactly two copies of each gene, or at most two copies of each gene (with missing gene copies). These algorithms are shown time-efficient and very accurate for both the rearrangement and DCJ distances.
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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 | 0.000 |
| 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 it