Assessment of the DNA barcode libraries for the study of the poorly-known rove beetle (Staphylinidae) fauna of West Siberia
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Bibliographic record
Abstract
Staphylinidae, or rove beetles, are one of the mega-diverse and abundant families of the ground-living terrestrial arthropods that is taxonomically poorly known even in the regions adjacent to Europe where the fauna has been investigated for the longest time. Since DNA barcoding is a tool to accelerate biodiversity research, here we explored if the currently-available COI barcode libraries are representative enough for the study of rove beetles of West Siberia. This is a vast region adjacent to Europe with poorly-known fauna of rove beetles and from where not a single DNA barcode has hitherto been produced for Staphylinidae. First, we investigated the faunal similarity between the rove beetle faunas of the climatically compatible West Siberia in Asia, Fennoscandia in Europe and Canada and Alaska in North America. Second, we investigated barcodes available for Staphylinidae from the latter two regions in BOLD and GenBank, the world's largest DNA barcode libraries. We conclude that the rather different rove beetle faunas of Fennoscandia, on the one hand and Canada and Alaska on the other hand, are well covered in both barcode libraries that complement each other. We also find that even without any barcodes originating from specimens collected in West Siberia, this coverage is helpful for the study of rove beetles there due to the significant number of widespread species shared between West Siberia and Fennoscandia and due to the even larger number of shared genera amongst all three investigated regions. For the first time, we compiled a literature-based checklist for 726 species of the West Siberian Staphylinidae supplemented by their occurrence dataset submitted to GBIF. Our script written for mining unique (i.e. not redundant) barcodes for a given geographic area across global libraries is made available here and can be adopted for any other regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it