Stability Analysis of Advanced Chilli Lines
Bibliographic record
Abstract
crop and pay good returns to growers.Chillies are grown at larger scale in Pakistan, occupying the major zone after onion and potato (Altaf et al., 2019).Chillies are acquired seasonally but consumed right through the year.Chillies production has been mounting since late 1990s.Currently it increased approximately up to 7 million tons per year from 2.5 million tonnes in the last decade.India is the world's leading chillies grower with annual production of Abstract | Chillies enriched with Vitamin C and beta-carotene, traditionally are the integral part of daily food in Pakistan.Like any other agricultural produce, chillies too have been greatly affected by erratic environmental conditions.So, it is important to assess yield stability in diverse environments.Therefore, stability analysis of six chilli advance breeding lines; NARC Chilli-1, NARC Chilli-2, NARC Chilli-3, NARC Chilli-4, NARC Chilli-5, NARC Chilli-6 was carried out.F 1 hybrid 'Big Daddy' was used as check.Plant material was figured out under Randomised complete Block Design with three replications and four locations; Chakwal, Faisalabad, Islamabad and Swat.Combined analysis of variance showed that mean yield of chilli advance lines at four locations were statistically significant.Genotype x locaton had significant interaction suggesting inconsistent performance of chilli genotypes over environments.The NARC Chilli-2 has regression slope equal to one with relatively low Wricke's Ecovalence, low value of Shukla's Stability Variance, relatively less value of deviation from regression and highest value of coefficient of determination.Results of stability analysis revealed that NARC Chilli-2 was the most stable line with reference to yield and this could be recomended for planting under different type of sites.NARC Chilli-6 and NARC Chilli-4 could be suitable for adequate environmental conditions while NARC Chilli-1, NARC Chilli-3 and NARC Chilli-5 for inadequate environmental conditions.Environment of Chakwal was found most productive and that of Faisalabad was poorest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".