Genetic diversity of<i>Carica papaya</i>as revealed by AFLP markers
Bibliographic record
Abstract
Genetic relationships among Carica papaya cultivars, breeding lines, unimproved germplasm, and related species were established using amplified fragment length polymorphism (AFLP) markers. Seventy-one papaya accessions and related species were analyzed with nine EcoRI-MseI primer combinations. A total of 186 informative AFLP markers was generated and analyzed. Cluster analysis suggested limited genetic variation in papaya, with an average genetic similarity among 63 papaya accessions of 0.880. Genetic diversity among cultivars derived from the same or similar gene pools was smaller, such as Hawaiian Solo hermaphrodite cultivars and Australian dioecious cultivars with genetic similarity at 0.921 and 0.912, respectively. The results indicated that self-pollinated hermaphrodite cultivars were as variable as open-pollinated dioecious cultivars. Genetic diversity between C. papaya and six other Carica species was also evaluated. Carica papaya shared the least genetic similarity with these species, with an average genetic similarity of 0.432; the average genetic similarity among the six other species was 0.729. The results from AFLP markers provided detailed estimates of the genetic variation within and among papaya cultivars, and supported the notion that C. papaya diverged from the rest of Carica species early in the evolution of this genus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".