Revision and annotation of DNA barcode records for marine invertebrates: report of the 8 <sup>th</sup> iBOL conference hackathon
Bibliographic record
Abstract
Abstract The accuracy of the identification of unknown specimens using DNA barcoding and metabarcoding relies on reference libraries containing records with reliable taxonomy and sequence quality. A rampant growth in barcode data led to a stringent need for data curation, especially in taxonomically difficult groups such as marine invertebrates. A major effort in curating marine barcode data deposited in the Barcode of Life Data Systems (BOLD) has been undertaken during the 8 th International Barcode of Life Conference (Trondheim, Norway, 2019). For practical reasons, only major taxonomic groups were reviewed and annotated (crustaceans, echinoderms, molluscs, and polychaetes). The congruence of Linnean names with Barcode Index Numbers (BINs) was investigated, and the records deemed uncertain were annotated with four tags: a) MIS-ID (misidentified, mislabeled or contaminated records), b) AMBIG (ambiguous records unresolved with the current data), c) COMPLEX (species occurring in multiple BINs), and d) SHARE (barcodes shared between species). A total of 83,712 specimen records corresponding to 7,576 species were reviewed and 39% of the species were tagged (7% MIS-ID, 17% AMBIG, 14% COMPLEX, and 1% SHARE). High percentages (>50%) of AMBIG tags were recorded in gastropods, whereas COMPLEX tags dominated in crustaceans and polychaetes. This high proportion of tagged species reflects either flaws in the barcoding workflow (e.g., misidentification, cross -contamination) or taxonomic difficulties (e.g., synonyms, undescribed species). Although data curation is crucial for barcode applications, such manual efforts of reviewing large datasets are not sustainable and the implementation of automated solutions to the furthest possible extent is hi ghly desirable.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".