A laboratory guide for generating DNA barcodes in grasses: a case study of<i>Leptochloa</i>s.l. (Poaceae: Chloridoideae)
Bibliographic record
Abstract
There is no easy way to identify to species, a small, vegetative leaf or culm sample of a grass and there are more than 12,000 species in this large, important family. The long-range aim of our study is to produce a standard DNA barcode library available to the public for all grasses (±1960 species) in North America (includes all Canada, Mexico and USA) that will facilitate the easy identification of these morphologically cryptic species. We provide a detailed protocol of the laboratory procedures for DNA extraction in grasses and the DNA-specific primers used for the polymerase chain reaction (PCR) enabling the laboratory work to be performed in any well-supplied molecular laboratory. In this paper we present a test of four barcodes [matK, rbcL, rpl32-trnL and internal transcribed spacer (ITS)] to discriminate among 50 taxa of grasses (55 samples), predominately in the subfamily Chloridoideae, and we used a tree-based method to identify relationships among species of Leptochloa sensu lato. The sequence divergence or discriminatory power based on uncorrected p-value, among the four DNA sequence markers was greatest in ITS (96%), followed by rpl32-trnL (25.6%), matK (3.0%) and rbcL (0.0%). matK was twice as effective in discriminating among the species compared with rbcL; rpl32-trnL was nearly 3.4 times better than rbcL; and nuclear rDNA ITS was 14 times better than rbcL. There are significant threshold levels of 0.0682 for ITS and 0.0732 for ITS + rpl32-trnL between intrageneric and intergeneric sequence divergences within the 16 species of Dinebra and between Dinebra and Diplachne, Disakisperma and Leptochloa sensu stricto. In our tree-based analyses of Leptochloa s.l. the following number of nodes with strong support (PP = 0.95−1.00) were successfully recovered (in descending order): combined ITS + rpl32-trnL, 43; ITS, 34; rp32-trnL, 27; matK, 19; and rbcL, 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".