<i>Drosophila</i>genomes and the development of affordable molecular markers for species genotyping
Bibliographic record
Abstract
The recent completion of genome sequencing of 12 species of Drosophila has provided a powerful resource for hypothesis testing, as well as the development of technical tools. Here we take advantage of genome sequence data from two closely related species of Drosophila, Drosophila simulans and Drosophila sechellia, to quickly identify candidate molecular markers for genotyping based on expected insertion or deletion (indel) differences between species. Out of 64 candidate molecular markers selected along the second and third chromosome of Drosophila, 51 molecular markers were validated using PCR and gel electrophoresis. We found that the 20% error rate was due to sequencing errors in the genome data, although we cannot rule out possible indel polymorphisms. The approach has the advantage of being affordable and quick, as it only requires the use of bioinformatics tools for predictions and a PCR and agarose gel based assay for validation. Moreover, the approach could be easily extended to a wide variety of taxa with the only limitation being the availability of complete or partial genome sequence data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".