Testing candidate plant barcode regions in the Myristicaceae
Bibliographic record
Abstract
The concept and practice of DNA barcoding have been designed as a system to facilitate species identification and recognition. The primary challenge for barcoding plants has been to identify a suitable region on which to focus the effort. The slow relative nucleotide substitution rates of plant mitochondria and the technical issues with the use of nuclear regions have focused attention on several proposed regions in the plastid genome. One of the challenges for barcoding is to discriminate closely related or recently evolved species. The Myristicaceae, or nutmeg family, is an older group within the angiosperms that contains some recently evolved species providing a challenging test for barcoding plants. The goal of this study is to determine the relative utility of six coding (Universal Plastid Amplicon - UPA, rpoB, rpoc1, accD, rbcL, matK) and one noncoding (trnH-psbA) chloroplast loci for barcoding in the genus Compsoneura using both single region and multiregion approaches. Five of the regions we tested were predominantly invariant across species (UPA, rpoB, rpoC1, accD, rbcL). Two of the regions (matK and trnH-psbA) had significant variation and show promise for barcoding in nutmegs. We demonstrate that a two-gene approach utilizing a moderately variable region (matK) and a more variable region (trnH-psbA) provides resolution among all the Compsonuera species we sampled including the recently evolved C. sprucei and C. mexicana. Our classification analyses based on nonmetric multidimensional scaling ordination, suggest that the use of two regions results in a decreased range of intraspecific variation relative to the distribution of interspecific divergence with 95% of the samples correctly identified in a sequence identification analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".