Barcoding Fauna Bavarica: Myriapoda – a contribution to DNA sequence-based identifications of centipedes and millipedes (Chilopoda, Diplopoda)
Bibliographic record
Abstract
We give a first account of our ongoing barcoding activities on Bavarian myriapods in the framework of the Barcoding Fauna Bavarica project and IBOL, the International Barcode of Life. Having analyzed 126 taxa (including 122 species) belonging to all major German chilopod and diplopod lineages, often using four or more specimens each, at the moment our species stock includes 82% of the diplopods and 65% of the chilopods found in Bavaria, southern Germany. The partial COI sequences allow correct identification of more than 95% of the current set of Bavarian species. Moreover, most of the myriapod orders and families appear as distinct clades in neighbour-joining trees, although the phylogenetic relationships between them are not always depicted correctly. We give examples of (1) high interspecific sequence variability among closely related species; (2) low interspecific variability in some chordeumatidan genera, indicating that recent speciations cannot be resolved with certainty using COI DNA barcodes; (3) high intraspecific variation in some genera, suggesting the existence of cryptic lineages; and (4) the possible polyphyly of some taxa, i.e. the chordeumatidan genus Ochogona. This shows that, in addition to species identification, our data may be useful in various ways in the context of species delimitations, taxonomic revisions and analyses of ongoing speciation processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".