Optimization of bulked AFLP analysis and its application for exploring diversity of natural and cultivated populations of red clover
Bibliographic record
Abstract
Landraces and wild populations of red clover (Trifolium pratense L.) may represent a significant yet poorly characterized genetic resource of temperate grasslands. A bulking strategy with amplified fragment length polymorphism (AFLP) markers was optimized to characterize 120 red clover populations in 6 different groups: Swiss wild clover populations, Mattenklee landraces, Mattenklee cultivars, field clover cultivars, Dutch wild clover populations, and Dutch landraces. Analysis of 2 bulked samples/population consisting of 20 plants each with12 AFLP primer combinations was found optimal for determining genetic diversity and relationships within and among red clover populations and groups. Swiss wild clover populations were clearly separated from all other red clover groups and variability within and among populations was shown to be particularly high in wild clover populations and Mattenklee landraces, emphasising their value as genetic resources for improvement of red clover cultivars, as well as for conservation and restoration of biodiversity. This study shows that the ancestry of red clover landraces is primarily found in introduced cultivars rather than in natural wild clover populations. In addition, the methodological considerations presented here may help improve diversity analyses using bulked samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".