Revealing the Hyperdiverse Mite Fauna of Subarctic Canada through DNA Barcoding
Bibliographic record
Abstract
Although mites are one of the most abundant and diverse groups of arthropods, they are rarely targeted for detailed biodiversity surveys due to taxonomic constraints. We address this gap through DNA barcoding, evaluating acarine diversity at Churchill, Manitoba, a site on the tundra-taiga transition. Barcode analysis of 6279 specimens revealed nearly 900 presumptive species of mites with high species turnover between substrates and between forested and non-forested sites. Accumulation curves have not reached an asymptote for any of the three mite orders investigated, and estimates suggest that more than 1200 species of Acari occur at this locality. The coupling of DNA barcode results with taxonomic assignments revealed that Trombidiformes compose 49% of the fauna, a larger fraction than expected based on prior studies. This investigation demonstrates the efficacy of DNA barcoding in facilitating biodiversity assessments of hyperdiverse taxa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".