Nature and magnitude of genetic variability and diversity analysis of Indian turmeric accessions using agro-morphological descriptors
Bibliographic record
Abstract
Turmeric, a vegetatively propagated crop, may have restricted variability from which to breed new cultivars. Understanding the genetic variability of a species is crucial for the progress of a genetic breeding program and requires characterization and evaluation of accessions. The objectives of this study were to determine extent of variability, relationships between different agro-morphological traits, and diversity among 25 different accessions of turmeric. The present experiment was conducted at the Vegetable Research Farm, Department of Horticulture, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad, India during 2008–2009 and 2009–2010. Accessions were arranged in a randomized complete block design with three replications. Significant mean square of accessions for all the traits studied indicates the existence of sufficient genetic variability among the studied accessions. The rhizome yield exhibited highly significant and positive association with plant height, number of leaves plant −1 , number of tillers plant −1 , weight of rhizomes plant −1 , length of primary rhizome, and dry matter recovery. The highest positive direct effect on rhizome yield was exerted by plant height. Multivariate analysis techniques allowed an effective study of genetic divergence and the grouping of the 25 accessions into six clusters. The highest inter-cluster distance was observed between cluster II and IV, accessions from these clusters can be used as potential parents for future breeding programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".