Low genetic variability of<i>Striga gesnerioides</i>populations parasitic on cowpea might be explained by a recent origin
Bibliographic record
Abstract
DubeM‐P & BelzileFJ (2010). Low genetic variability ofStriga gesnerioidespopulations parasitic on cowpea might be explained by a recent origin.Weed Research50, 493–502. Summary Striga gesnerioidesis an obligate root hemiparasitic plant that causes considerable yield losses to cowpea, an important crop legume of Sub‐Saharan Africa. The use of resistant cultivars is the easiest and most effective method to control the parasite. Several cowpea cultivars exhibiting resistance have been identified during the last decades. However, most resistant cultivars show a differential response when grown in different countries across West Africa, suggesting that there are different races ofS. gesnerioides. In this study, we investigated the genetic variability within and between 43 populations of five of the previously recognised races of the parasite present in West Africa. Amplified fragment length polymorphism (AFLP) markers were used on up to 10 individuals from each population. These markers showed almost no genetic variability within populations. The variability between the populations was also extremely low and did not allow discrimination of the five races. There was a certain geographical structure, but no ‘racial’ clustering could be seen. Even AFLP markers previously reported to be race‐specific on another set ofStrigapopulations proved unable to discriminate between races in this collection of populations. Possible causes of the low level of genetic variability include the hypothesis that this strain has only quite recently arisen. Such a low level of variability and the absence of specific markers for the virulence will have consequences on the evolution of the parasite and on the development of adequate control methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".