DNA barcodes for Mexican Cactaceae, plants under pressure from wild collecting
Bibliographic record
Abstract
DNA barcodes could be a useful tool for plant conservation. Of particular importance is the ability to identify unknown plant material, such as from customs seizures of illegally collected specimens. Mexican cacti are an example of a threatened group, under pressure because of wild collection for the xeriscaping trade and private collectors. Mexican cacti also provide a taxonomically and geographically coherent group with which to test DNA barcodes. Here, we sample the matK barcode for 528 species of Cactaceae including approximately 75% of Mexican species and test the utility of the matK region for species-level identification. We find that the matK DNA barcode can be used to identify uniquely 77% of species sampled, and 79-87% of species of particular conservation importance. However, this is far below the desired rate of 95% and there are significant issues for PCR amplification because of the variability of primer sites. Additionally, we test the nuclear ITS regions for the cactus subfamily Opuntioideae and for the genus Ariocarpus (subfamily Cactoideae). We observed higher rates of variation for ITS (86% unique for Opuntioideae sampled) but a much lower PCR success, encountering significant intra-individual polymorphism in Ariocarpus precluding the use of this marker in this taxon. We conclude that the matK region should provide useful information as a DNA barcode for Cactaceae if the problems with primers can be addressed, but matK alone is not sufficiently variable to achieve species-level identification. Additional complementary regions should be investigated as ITS is shown to be unsuitable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".