Disruptive selection for sternopleural bristle phenotypes in<i>Drosophila ananassae</i>
Bibliographic record
Abstract
Disruptive selection is potentially critical in maintaining variation and initiating speciation and plays an important role in the organization of genetic variability in natural populations. It occurs when extreme phenotypes have a fitness advantage over intermediate phenotypes. Disruptive selection for high and low numbers of sternopleural bristles in Drosophila ananassae was applied for 12 generations to test its effect in induction of behavioural isolation. Pattern of mating between flies of high and low lines was tested in an Elens-Wattiaux mating chamber by using a multiple-choice technique after G(5) and G(12). Data was analyzed by calculating χ(2) under the assumption of random mating to test the difference between homoand heterogamic matings, and sexual isolation was tested by calculating the isolation estimate. The results show that there is no evidence for sexual isolation in G(5) and G(12). The realized heritability, standard error of regression coefficient, and t values suggest that disruptive selection for sternopleural bristle phenotypes was effective, but it does not lead to behavioral isolation in D. ananassae. Rather, it induces differences in mating propensity, which is influenced by sternopleural bristle phenotypes.
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.
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 | 0.000 | 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 | 0.001 |
| 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".