Efficiency of three PCR-based markers in assessing genetic variation among cowpea (<i>Vigna unguiculata</i>subsp.<i>unguiculata</i>) landraces
Bibliographic record
Abstract
The main objective of this study was to investigate the efficiency of RAPD, AFLP, and SAMPL marker systems in detecting genetic polymorphism in cowpea landraces (Vigna unguiculata subsp. unguiculata (L.) Walp.) that probably share a similar genetic pool. A second objective was to determine the level of diversity among landraces from a restricted area, to define the most appropriate strategy of on-farm conservation. Each marker system was able to discriminate among the materials analysed, but a clear distinction between all the local varieties was only obtained with AFLP and SAMPL markers. The average diversity index was quite similar for each marker system, but owing to the differences in the effective multiplex ratio values the marker index was higher for the AFLP and SAMPL systems than for the RAPD system. The AFLP and SAMPL techniques appear to be more useful than the RAPD technique in the analysis of limited genetic diversity among the cowpea landraces tested. The significant correlations of SAMPL similarity and cophenetic matrices with those of the other markers, and the lower number of primer combinations required, indicate that this technique is the most valuable. The low genetic similarity detected among landraces suggests that all the cowpea landraces should be maintained on the respective farms from which they came.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".