Genetic diversity among varieties of the native forage grass <i>Trichloris crinita</i> based on AFLP markers, morphological characters, and quantitative agronomic traits
Bibliographic record
Abstract
We assessed the genetic diversity in Trichloris crinita (Poaceae) varieties from South America, using AFLPs, morphological characters, and quantitative agronomic traits. Owing to the importance of this species for range grazing, we first characterized the varieties based on forage productivity. Biomass production varied 9 fold among the materials evaluated. Analysis of AFLP fingerprints allowed the discrimination of all varieties with a few selected primer combinations. Pair-wise genetic similarities, using marker data, ranged from 0.31 to 0.92 (Jaccard coefficients). Marker-based unweighted pair group method with arithmetic averaging (UPGMA) cluster analysis did not show geographical clustering, but rather grouped the varieties according to their biomass production. We identified 18 markers associated with biomass production, of which 8 showed complete correlation (r = 1.00) with this trait. These DNA markers can be used to assist selection for high forage productivity in T. crinita. Cluster analysis using morphological and quantitative characters revealed 4 distinct groups of varieties, clearly separated according to their biomass yield. The variables foliage height and basal diameter were strongly correlated with biomass production and these phenotypic markers can be used to select productive plants. The relations among the varieties based on AFLP data were significantly correlated with those based on agronomic and morphological characters, suggesting that the 2 systems give similar estimates of genetic relations among the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".