Data from: Comparison of five methods for delimitating species in Ophion Fabricius, a diverse genus of parasitoid wasps (Hymenoptera, Ichneumonidae)
Bibliographic record
Abstract
DNA taxonomy has been proposed as a method to quickly assess diversity and species limits in highly diverse, understudied taxa. Here we use five methods for species delimitation and two genetic markers (COI and ITS2) to assess species diversity within the parasitoid genus, Ophion. We searched for compensatory base changes (CBC’s) in ITS2, and determined that they are too rare to be of practical use in delimiting species in this genus. The other four methods used both COI and ITS2, and included distance-based (threshold analysis and ABGD) and tree-based (GMYC and PTP) models. We compared the results of these analyses to each other under various parameters and tested their performance with respect to 11 Nearctic species/morphospecies and 15 described Palearctic species. We also computed barcode accumulation curves of COI sequences to assess the completeness of sampling. The species count was highly variable depending on the method and parameters used, ranging from 47 to 168 species, with more conservative estimates of 89–121 species. Despite this range, many of the Nearctic test species were fairly robust with respect to method. We concluded that while there was often good congruence between methods, GMYC and PTP were less reliant on arbitrary parameters than the other two methods and more easily applied to genetic markers other than COI. However, PTP was less successful at delimiting test species than was GMYC. All methods, as well as the barcode accumulation curves, indicate that several Palearctic species remain undescribed and that we have scarcely begun to appreciate the Nearctic diversity within this genus.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".