Evaluating the Utility of Single-Locus DNA Barcoding for the Identification of Ribbon Worms (Phylum Nemertea)
Bibliographic record
Abstract
Whereas many nemerteans (ribbon worms; phylum Nemertea) can be identified from external characters if observed alive, many are still problematic. When it comes to preserved specimens (as in e.g. marine inventories), there is a particular need for specimen identifier alternatives. Here, we evaluate the utility of COI (cytochrome c oxidase subunit I) as a single-locus barcoding gene. We sequenced, data mined, and compared gene fragments of COI for 915 individuals representing 161 unique taxonomic labels for 71 genera, and subjected different constellations of these to both distance-based and character-based DNA barcoding approaches, as well as species delimitation analyses. We searched for the presence or absence of a barcoding gap at different taxonomic levels (phylum, subclass, family and genus) in an attempt to understand at what level a putative barcoding gap presents itself. This was performed both using the taxonomic labels as species predictors and using objectively inferred species boundaries recovered from our species delimitation analyses. Our data suggest that COI works as a species identifier for most groups within the phylum, but also that COI data are obscured by misidentifications in sequence databases. Further, our results suggest that the number of predicted species within the dataset is (in some cases substantially) higher than the number of unique taxonomic labels-this highlights the presence of several cryptic lineages within well-established taxa and underscores the urgency of an updated taxonomic backbone for the phylum.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".