BOLDistilled: Comprehensive but compact DNA barcode reference libraries
Bibliographic record
Abstract
Advances in DNA sequencing technology have stimulated the rapid uptake of protocols—such as eDNA analysis and metabarcoding—that infer the species composition of environmental samples from DNA sequences. DNA barcode reference libraries play a critical role in the interpretation of sequences gathered through such protocols, but many lack adequate taxonomic curation, include redundant records, do not support end-user analytical pipelines, and are not permanently archived in repositories. Furthermore, because DNA sequencers are outpacing Moore’s Law and reference libraries are rapidly expanding, the computational power required to assign sequences to source taxa increases yearly. To address these limitations while also providing access to anonymized private data from the Barcode of Life Data System (BOLD), we introduce an algorithmic approach to construct DNA barcode reference libraries that overcome the above issues. Hosted online, ‘BOLDistilled’ libraries are comprehensive but compact, because the algorithm distills genetic variation into a minimal set of records. We generated a BOLDistilled library for the barcode region of the cytochrome c oxidase 1 gene (COI) based on all data in BOLD. This library contains 1.2M records versus 17.5M in the complete library, a compression which reduced the time required for sequence analysis of metabarcoded samples by ≥98% with no reduction in the accuracy of taxonomic placements. BOLDistilled libraries will be updated routinely, with the current version and all previous versions available at boldsystems.org/BOLDistilled. By providing access to persistent, comprehensive, and high-quality reference data, BOLDistilled libraries will strengthen the capacity of DNA-based identification systems to advance biodiversity science.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.024 |
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".