EXPLORING GENOME REARRANGEMENTS USING VIRTUAL HYBRIDIZATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genomes evolve with both mutations and large scale events, such as inversions, translocations, duplications and losses, that modify the structure of a set of chromosomes. In order to study these types of large-scale events, the first task is to select, in different genomes, sub-sequences that are considered “equivalent”. Many approaches have been used to identify equivalent sequences, either based on biological experiments, gene annotations, or sequence alignments. These techniques suffer from a variety of drawbacks that often result in the impossibility, for independent researchers, to reproduce the datasets used in the studies, or to adapt them to newly sequenced genomes. In this paper, we show that carefully selected small probes can be efficiently used to construct datasets. Once a set of probes is identified – and published –, datasets for whole genome comparisons can be produced, and reproduced, with elementary algorithms; decisions about what is considered an occurrence of a probe in a genome can be criticized and reevaluated; and the structure of a newly sequenced genome can be obtained rapidly, without the need of gene annotations or intensive computations. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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