With a little help from DNA barcoding: investigating the diversity of Gastropoda from the Portuguese coast
Bibliographic record
Abstract
The Gastropoda is one of the best studied classes of marine invertebrates. Yet, most species have been delimited based on morphology only. The application of DNA barcodes has shown to be greatly useful to help delimiting species. Therefore, sequences of the cytochrome c oxidase I gene from 108 specimens of 34 morpho-species were used to investigate the molecular diversity within the gastropods from the Portuguese coast. To the above dataset, we added available COI-5P sequences of taxonomically close species, in a total of 58 morpho-species examined. There was a good match between ours and sequences from independent studies, in public repositories. We found 32 concordant (91.4%) out of the 35 Barcode Index Numbers (BINs) generated from our sequences. The application of a ranking system to the barcodes yield over 70% with top taxonomic congruence, while 14.2% of the species barcodes had insufficient data. In the majority of the cases, there was a good concordance between morphological identification and DNA barcodes. Nonetheless, the discordance between morphological and molecular data is a reminder that even the comparatively well-known European marine gastropods can benefit from being probed using the DNA barcode approach. Discordant cases should be reviewed with more integrative studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 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".