Globally optimal classification and pairing of human chromosomes
Why this work is in the frame
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Bibliographic record
Abstract
We investigate globally optimal algorithms for automated classification and pairing of human chromosomes. Even in cases where the cell data are incomplete as often encountered in practice, we can still formulate the problem as a transportation problem, and hence find the globally optimal solution in polynomial time. In addition, we propose a technique of homologue pairing via maximum-weight graph matching. It obtains the globally optimal solution by forming all homologue pairs simultaneously under a maximum likelihood criterion, rather than finding one pair at a time as in existing heuristic algorithms. After the optimal homologue pairing, chromosome classification can also be done by maximum-weight graph matching. This new graph theoretical approach to chromosome pairing and classification is more robust than the transportation algorithm, because many attributes of a chromosome have less variations within a cell than between different cells.
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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