Analysis of Correlation and Regression among M2 Wheat Mutant Population for Yield and its Associated Traits
Bibliographic record
Abstract
Crop yield is the resultant product of components character which is not under the control of any single gene, therefore it is necessary for plant breeder to know the relationship between two traits. The present research was conducted to calculate the correlation and regression for yield and yield contributing traits in mutant population of bread wheat. The experiment was conducted at Nuclear Institute of Agriculture (NIA), Tandojam, during rabi season 2015-2016, in split plot design with three replications. Material under study was two wheat varieties (T.D-1 and ESW-9525). These two wheat varieties were evaluated along with control for yield and yield associated traits under normal field conditions. Mean square showed that there were significant differences between wheat varieties for days to 75% heading, days to 75% maturity, plant height (cm), spikelets spike-1, grains spike-1, 1000 grain weight (g), biological yield plant-1 (g), harvest index (%), spike length (cm) and grain yield plant-1 (g). Number of grains spike-1 showed positive and highly significant correlation with spike length, spikelets spike-1, 1000 grain weight and biological yield of plant-1. Significant and positive with days to 75% maturity and harvest index. However, negative correlation was found with height of plant and non-significant but negative with the trait of days to 75% heading. Grain yield plant-1 showed highly positive highly significant association with grains spike-1, number of spikelets spike-1, length of spike, harvest index and biological yield plant-1 and negative with plant height. Approximately 85.9%, 65.2%, 59% and 24.3% variation in grain yield plant-1 is due to grains spike-1, biological yield plant-1, harvest seed index and height of plant, respectively. This shows that taller plants produce lesser grain yield.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".