A workflow for expanding DNA barcode reference libraries through ‘museum harvesting’ of natural history collections
Bibliographic record
Abstract
Natural history collections are the physical repositories of our knowledge on species, the entities of biodiversity. Making this knowledge accessible to society - through, for example, digitisation or the construction of a validated, global DNA barcode library - is of crucial importance. To this end, we developed and streamlined a workflow for 'museum harvesting' of authoritatively identified Diptera specimens from the Smithsonian Institution's National Museum of Natural History. Our detailed workflow includes both on-site and off-site processing through specimen selection, labelling, imaging, tissue sampling, databasing and DNA barcoding. This approach was tested by harvesting and DNA barcoding 941 voucher specimens, representing 32 families, 819 genera and 695 identified species collected from 100 countries. We recovered 867 sequences (> 0 base pairs) with a sequencing success of 88.8% (727 of 819 sequenced genera gained a barcode > 300 base pairs). While Sanger-based methods were more effective for recently-collected specimens, the methods employing next-generation sequencing recovered barcodes for specimens over a century old. The utility of the newly-generated reference barcodes is demonstrated by the subsequent taxonomic assignment of nearly 5000 specimen records in the Barcode of Life Data Systems.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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".