Developing EPIC markers for chalcidoid Hymenoptera from EST and genomic data
Bibliographic record
Abstract
Increasing numbers of phylogeographic studies make comparative inferences about the histories of co-distributed species. Although the aims of such studies are best achieved by jointly analysing sequences from multiple loci in a model-based framework, such data currently exist for few nonmodel systems. We used existing genomic data and expressed sequence tags (ESTs) for Hymenoptera and other insects to design intron-crossing primers for 40 loci, mainly ribosomal proteins, for chalcidoid parasitoids. Amplification success was scored on a range of taxa associated with two natural communities; oak galls and figs. Taxa were chosen at increasing distance from Nasonia, which was used for primer design, (i) within Pteromalids, (ii) within Chalcidoidea (Eupelmidae, Eulophidae, Eurytomidae, Ormyridae, Torymidae) and (iii) for a selection of distantly related gall and fig wasps (Cynipidae, Agaonidae). To assess the utility of these loci for phylogeographic and population genetic studies, we compared genetic diversity between Western Palaearctic refugia for two species. Our results show that it is feasible to design a large number of exon-primed-intron-crossing (EPIC) loci that may be informative about phylogeographic history within species but amplify across a large taxonomic range.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".