Marine ecozones represent a major gap in Canadian DNA barcoding efforts
Bibliographic record
Abstract
Because of Canada’s large size, it is impractical to obtain a comprehensive perspective on biotic change through morphological approaches. DNA metabarcoding offers a potential path, but its application requires access to a well-parameterized DNA barcode reference library. This study presents the current state of DNA barcode coverage for Canadian animals, highlighting progress, identifying gaps, and providing recommendations for future research. Our analysis indicates that many of the known species (100 000 terrestrial and 6000 marine) in the Canadian fauna possess DNA barcode coverage, but there are important gaps geographically and taxonomically. We summarize DNA barcode coverage for the species in freshwater, marine, and terrestrial environments by ecoregion, finding that 95.6% of the 2.3 million Canadian barcode records derive from terrestrial ecosystems. Although the density of barcode records per 100 km² is 13x higher for terrestrial than aquatic environments (22.4 vs. 1.7), coverage for 58% of marine species is available (54% for annelids, 52% for arthropods, 88% for chordates, 39% for echinoderms, and 46% for molluscs). By revealing data-deficient areas and taxonomic groups, this study offers a roadmap for expanding the DNA barcode library for the Canadian fauna as an essential foundation for the scalable biosurveillance initiatives that inform biodiversity conservation efforts.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".