Genetic diversity evaluation of some agronomic traits in dill accessions using biplot model
Bibliographic record
Abstract
As a widely cultivated vegetable crop, dill (Anethum graveolens L.) is produced for both food and medicinal purposes. This research investigates the genetic diversity of 30 dill accessions from diverse regions of Iran. The evaluated traits included: seed number per umbel (SNU), number of umbellets per umbel (NUU), number of umbels per plant (NUP), node number before flowering (NNF), inflorescence fresh weight (IFW), plant fresh weight (PFW), inflorescence dry weight (IDW), plant dry weight (PDW), and seed yield (SY). The accession-by-trait biplot explained 83% (52% and 31% for the first and second principal components, respectively) of the total variation, indicating a complex pattern of interactions between accessions and traits in dill. Plant fresh and dry weight, and SY were related to seed of umbel, and umbellet of umbel, so these yield components can be used for improving both seed yield and biomass. Results revealed significant variation in traits across the accessions, with accessions 22 (Urmia) and 11 (Sarab) showing consistently high performance for both PDW and SY. Also, a graphical biplot analysis highlighted key trait associations, including strong positive relationships between IFW, IDW, and NUP. Accessions 22 and 11 were identified as the most reliable candidates for future breeding programs aimed at improving dill biomass and seed yield. Accession 24, followed by accessions 3, 22, and 30, were identified as the ideal accessions, suggesting their superior ability for discriminating across traits. PDW following to SNU and SY indicated the more potential for both representative and discrimination capabilities, positioned at the ideal trait location.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".