Inversion frequencies in<i>Drosophila serrata</i>along an eastern Australian transect
Bibliographic record
Abstract
Clinal patterns over broad geographic regions provide a way of identifying characteristics of species under selection and are increasingly being used in quantitative trait locus mapping of adaptive genetic variation in Drosophila. However, interpretations of clinal patterns can be complicated by inversions that also vary clinally and reduce recombination in some parts of the genome. Drosophila serrata (Malloch) is an Australian endemic species being used to investigate the genetic basis of geographic variation in climatic adaptation and mate recognition. Here we describe inversions in D. serrata populations from the east coast of Australia, covering tropical and temperate regions. Seven autosomal paracentric inversions and 1 apparently complex X chromosome arrangement were identified from these populations. All inverted arrangements were relatively more common in tropical populations; 2 common inversions showed clinal patterns over part of the range of D. serrata. Inversion polymorphism was relatively higher in tropical populations and almost absent in populations near the cooler southern border, in agreement with findings on other Drosophila species. While these patterns will complicate mapping of adaptive variation in D. serrata, they suggest that this species will be useful in investigatingthe dynamics of inversion-trait associations in natural populations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".