Building a DNA barcode library of Alaska’s non-marine arthropods
Bibliographic record
Abstract
Climate change may result in ecological futures with novel species assemblages, trophic mismatch, and mass extinction. Alaska has a limited taxonomic workforce to address these changes. We are building a DNA barcode library to facilitate a metabarcoding approach to monitoring non-marine arthropods. Working with the Canadian Centre for DNA Barcoding, we obtained DNA barcodes from recently collected and authoritatively identified specimens in the University of Alaska Museum (UAM) Insect Collection and the Kenai National Wildlife Refuge collection. We submitted tissues from 4776 specimens, of which 81% yielded DNA barcodes representing 1662 species and 1788 Barcode Index Numbers (BINs), of primarily terrestrial, large-bodied arthropods. This represents 84% of the species available for DNA barcoding in the UAM Insect Collection. There are now 4020 Alaskan arthropod species represented by DNA barcodes, after including all records in Barcode of Life Data Systems (BOLD) of species that occur in Alaska - i.e., 48.5% of the 8277 Alaskan, non-marine-arthropod, named species have associated DNA barcodes. An assessment of the identification power of the library in its current state yielded fewer species-level identifications than expected, but the results were not discouraging. We believe we are the first to deliberately begin development of a DNA barcode library of the entire arthropod fauna for a North American state or province. Although far from complete, this library will become increasingly valuable as more species are added and costs to obtain DNA sequences fall.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".