Starting a<scp>DNA</scp>barcode reference library for shallow water polychaetes from the southern European Atlantic coast
Bibliographic record
Abstract
Annelid polychaetes have been seldom the focus of dedicated DNA barcoding studies, despite their ecological relevance and often dominance, particularly in soft-bottom estuarine and coastal marine ecosystems. Here, we report the first assessment of the performance of DNA barcodes in the discrimination of shallow water polychaete species from the southern European Atlantic coast, focusing on specimens collected in estuaries and coastal ecosystems of Portugal. We analysed cytochrome oxidase I DNA barcodes (COI-5P) from 164 specimens, which were assigned to 51 morphospecies. To our data set from Portugal, we added available published sequences selected from the same species, genus or family, to inspect for taxonomic congruence among studies and collection location. The final data set comprised 290 specimens and 79 morphospecies, which generated 99 Barcode Index Numbers (BINs) within Barcode of Life Data Systems (BOLD). Among these, 22 BINs were singletons, 47 other BINs were concordant, confirming the initial identification based on morphological characters, and 30 were discordant, most of which consisted on multiple BINs found for the same morphospecies. Some of the most prominent cases in the latter category include Hediste diversicolor (O.F. Müller, 1776) (7), Eulalia viridis (Linnaeus, 1767) (2) and Owenia fusiformis (delle Chiaje, 1844) (5), all of them reported from Portugal and frequently used in ecological studies as environmental quality indicators. Our results for these species showed discordance between molecular lineages and morphospecies, or added additional relatively divergent lineages. The potential inaccuracies in environmental assessments, where underpinning polychaete species diversity is poorly resolved or clarified, demand additional and extensive investigation of the DNA barcode diversity in this group, in parallel with alpha taxonomy efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".