Genetic diversity estimates in <i>Cicer</i> using AFLP analysis
Bibliographic record
Abstract
Abstract Amplified fragment length polymorphism (AFLP) analysis was used to evaluate the genetic variation among cultivated chickpea and wild Cicer relatives. In total, 214 marker loci were assessed, of which 211 were polymorphic (98.6%) across the 95 accessions that represented 17 species of Cicer. The genetic variation within a species was highest in C. pinnatifidum followed by C. reticulatum and lowest in C. macracanthum. Three main species groups were identified by UPGMA clustering using Nei's pair‐wise distance calculations. Group I included the cultivated species C. arietinum, C. reticulatum and C. echinospermum. Within this group, C. reticulatum accessions were clustered closest to the C. arietinum cultivars ‘Lasseter’, ‘Kaniva’ and ‘Bumper’, supporting the hypothesis that C. reticulatum is the most probable progenitor of the cultivated species. Cicer bijugum, C. judaicum and C. pinnatifidum were clustered together creating group II. Group III contained all nine perennial species assessed and two annual species C. yamashitae and C. cuneatum. The genetic distance detected between group I and group III (0.13) was equivalent to the genetic distance detected between group I and group II (the primary and annual tertiary species, respectively; 0.14). This indicated that the perennial tertiary species may be as valuable for increasing variation to incorporate novel germplasm in the cultigen as the annual tertiary species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".