Removing Noise and Ambiguities from Comparative Maps in Rearrangement Analysis
Bibliographic record
Abstract
Comparison of genomic maps is hampered by errors and ambiguities introduced by mapping technology, incorrectly resolved paralogy, small samples of markers and extensive genome rearrangement. We design an analysis to remove or resolve most of these problems and to extract corrected data where markers occur in consecutive strips in both genomes. To do this we introduce the notion of pre-strip, an efficient way of generating these, and a compatibility analysis culminating in a Maximum Weighted Clique (MWC) search. The output can be directly analyzed with genome rearrangement algorithms, allowing the restoration of some of the data not incorporated into the clique solution. We investigate the trade-off between criteria for discarding excessive pre-strips to make MWC feasible, in terms of retaining as many markers as possible in the solution and producing an economical rearrangement analysis. We explore these questions through simulation and through comparison of the rice and sorghum genomes.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".