Biodiversity studies in<i>Phaseolus</i>species by DNA barcoding
Bibliographic record
Abstract
The potential of DNA barcoding was tested as a system for studying genetic diversity and genetic traceability in bean germplasm. This technique was applied to several pure lines of Phaseolus vulgaris L. belonging to wild, domesticated, and cultivated common beans, along with some accessions of Phaseolus coccineus L., Phaseolus lunatus L., and Vigna unguiculata (L.) Walp. A multilocus approach was exploited using three chloroplast genic regions (rbcL, trnL, and matK), four intergenic spacers (rpoB-trnC, atpBrbcL, trnT-trnL, and psbA-trnH), and nuclear ITS1 and ITS2 rDNA sequences. Our main goals were to identify the markers and SNPs that show the best discriminant power at the variety level in common bean germplasm, to examine two methods (tree based versus character based) for biodiversity analysis and traceability assays, and to evaluate the overall utility of chloroplast DNA barcodes for reconstructing the origins of modern Italian varieties. Our results indicate that the neighbor-joining method is a powerful approach for comparing genetic diversity within plant species, but it is relatively uninformative for the genetic traceability of plant varieties. In contrast, the character-based method was able to identify several distinct haplotypes over all target regions corresponding to Mesoamerican or Andean accessions; Italian accessions originated from both gene pools. On the whole, our findings raise some concerns about the use of DNA barcoding for intraspecific genetic diversity studies in common beans and highlights its limitations for resolving genetic relationships between landraces and varieties.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".