Assessing the efficiency of eDNA metabarcoding for documenting patterns of metazoan communities in the Arctic.
Bibliographic record
Abstract
Arctic biodiversity has been for a long time underestimated, situated in a region considered as an austere environment combined to the lack of knowledge. The analysis of environmental DNA (eDNA), a new method tracing DNA from macro-organisms, is changing the way we monitor aquatic biodiversity and has the potential to answer large-scale fundamental biodiversity questions in remote regions such as the Arctic. This study aims to evaluate the eDNA dispersal by contrasting the difference between eDNA metabarcoding and classical sampling methods of the alpha, beta and gamma biodiversity index across the Canadian Arctic latitudinal gradient. Water samples of 250mL were collected in 13 sites within three Arctic ports: Churchill, Iqaluit and Deception Bay. Our results showed a species richness of 422 marine invertebrates, while the number of species detected decreased with latitude with more than 100 less species in the northern port. Contrasting the alpha biodiversity index between eDNA metabarcoding and species spatial distribution may be used to evaluate the eDNA spatial dispersion, thus answering important questions related to the ecology of eDNA and improving the integration of this new molecular tool within applied sciences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".