A regional approach to plant DNA barcoding provides high species resolution of sedges (<i>Carex</i> and <i>Kobresia</i>, Cyperaceae) in the Canadian Arctic Archipelago
Bibliographic record
Abstract
Previous research on barcoding sedges (Carex) suggested that basic searches within a global barcoding database would probably not resolve more than 60% of the world's some 2000 species. In this study, we take an alternative approach and explore the performance of plant DNA barcoding in the Carex lineage from an explicitly regional perspective. We characterize the utility of a subset of the proposed protein-coding and noncoding plastid barcoding regions (matK, rpoB, rpoC1, rbcL, atpF-atpH, psbK-psbI) for distinguishing species of Carex and Kobresia in the Canadian Arctic Archipelago, a clearly defined eco-geographical region representing 1% of the Earth's landmass. Our results show that matK resolves the greatest number of species of any single-locus (95%), and when combined in a two-locus barcode, it provides 100% species resolution in all but one combination (matK + atpFH) during unweighted pair-group method with arithmetic mean averages (UPGMA) analyses. Noncoding regions were equally or more variable than matK, but as single markers they resolve substantially fewer taxa than matK alone. When difficulties with sequencing and alignment due to microstructural variation in noncoding regions are also considered, our results support other studies in suggesting that protein-coding regions are more practical as barcoding markers. Plastid DNA barcodes are an effective identification tool for species of Carex and Kobresia in the Canadian Arctic Archipelago, a region where the number of co-existing closely related species is limited. We suggest that if a regional approach to plant DNA barcoding was applied on a global scale, it could provide a solution to the generally poor species resolution seen in previous barcoding studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".