A revolutionary protocol to describe understudied hyperdiverse taxa and overcome the taxonomic impediment
Bibliographic record
Abstract
Here we elucidate and justify a DNA barcode approach to insect species description that can be applied to name tens of thousands of species of Ichneumonoidea and many other species-rich taxa. Each description consists of a lateral habitus image of the specimen, a COI barcode diagnosis, and the holotype specimen information required by the International Code of Zoological Nomenclature. We believe this approach, or a slight modification of it, will be useful for many other underdescribed hyperdiverse taxa, especially in the tropics. Due to the extreme species-richness of the Ichneumonoidea, the very low percentage of described species, and the lack of detailed biological information for most described species, the standard taxonomic approach is inefficient and overwhelmingly time consuming. A DNA barcode-based approach to initial description will provide a solid foundation of species hypotheses from which more comprehensive descriptions can be developed as other data, time, and budgets permit. Here we elucidate this view and detailed methodology that can generally be applied to species-rich underdescribed taxa. A real example is given by describing species in two genera, Hemichoma and Zelomorpha , reared from the Área de Conservación Guanacaste in northwestern Costa Rica. The generic type species Zelomorphaarizonensis is given a DNA barcode diagnosis and the following new species are described: Zelomorphaangelsolisi , Zelomorphabobandersoni , Zelomorphadanjohnsoni , Zelomorphadonwindsori , Zelomorphaeffugia , Zelomorphajohnchemsaki , Zelomorphakellyanneae , Zelomorphalarrykirkendalli , Zelomorphamariyavladmirovnae , Zelomorphamikeiviei , Zelomorphamyricagaleae , Zelomorphanoahjaneae , Zelomorphapaulgoldsteini , Zelomorphaterryerwini , Zelomorphawillsflowersi , Hemichomadonwhiteheadi , Hemichomafrankhovorei , and Hemichomajohnkingsolveri .
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".