A global view of genetic diversity in cultivated sorghums using a core collection
Bibliographic record
Abstract
We report here an analysis of the structure of genetic diversity in cultivated sorghums. A core collection of 210 landraces representative of race, latitude of origin, response to day length, and production system was analysed with 74 RFLP probes dispersed throughout the genome. Multivariate analyses showed the specificity of the subrace guinea margaritiferum, as well as the geographical and racial pattern of genetic diversity. Neighbour-joining analysis revealed a clear differentiation between northern and southern equatorial African accessions. The presence of Asian accessions in these 2 major geographical poles for sorghum evolution indicated two introductions of sorghum into Asia. Morphological race also influenced the pattern of sorghum genetic diversity. A single predominant race was identified in 8 of 10 clusters of accessions, i.e., 1 kafir, 1 durra, 4 guinea, and 2 caudatum clusters. Guinea sorghums, with the exception of accessions in the margaritiferum subrace, clustered in 3 geographical groups, i.e., western African, southern African, and Asian guinea clusters; the latter two appeared more closely related. Caudatum were mainly distributed in 2 clusters, the African Great Lakes caudatum cluster and those African caudatum originating from other African regions. This last differentiation appears related to contrasting photoperiod responses. These results aid in the optimization of sampling accessions for introgression in breeding programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".