Plant DNA barcodes and species resolution in sedges (<i>Carex</i>, Cyperaceae)
Bibliographic record
Abstract
We investigate the species discriminatory power of a subset of the proposed plant barcoding loci (matK, rbcL, rpoC1, rpoB, trnH-psbA) in Carex, a cosmopolitan genus that represents one of the three largest plant genera on earth (c. 2000 species). To assess the ability of barcoding loci to resolve Carex species, we focused our sampling on three of the taxonomically best-known groups in the genus, sections Deweyanae (6/8 species sampled), Griseae (18/21 species sampled), and Phyllostachyae (10/10 species sampled). Each group represents one of three major phylogenetic lineages previously identified in Carex and its tribe Cariceae, thus permitting us to evaluate the potential of DNA barcodes to broadly identify species across the tribe and to differentiate closely related sister species. Unlike some previous studies that have suggested that plant barcoding could achieve species identification rates around 90%, our results suggest that no single locus or multilocus barcode examined will resolve much greater than 60% of Carex species. In fact, no multilocus combination can significantly increase the resolution and statistical support (i.e., ≥ 70% bootstrap) for species than matK alone, even combinations involving the second most variable region, trnH-psbA. Results suggest that a matK barcode could help with species discovery as 47% of Carex taxa recently named or resolved within cryptic complexes in the past 25 years also formed unique species clusters in upgma trees. Comparisons between the nrDNA internal transcribed spacer region (ITS) and matK in sect. Phyllostachyae suggest that matK not only discriminates more species (50-60% vs. 25%), but it provides more resolved phylogenies than ITS. Given the low levels of species resolution in rpoC1 and rpoB (0-13%), and difficulties with polymerase chain reaction amplification and DNA sequencing in rbcL and trnH-psbA (alignment included), we strongly advocate that matK should be part of a universal plant barcoding system. Although identification rates in this study are low, they can be significantly improved by a regional approach to barcoding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".