ENTRY BY TESTER BIPLOT MODEL FOR EVALUATION OF SOME KABULI CHICKPEA GENOTYPES BASED ON SEVERAL MULTIPLE TRAITS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of investigation was to evaluate the pattern of genetic variation in Kabuli chickpea genotypes through various traits under semi-arid rainfed circumstances.Trial was performed to evaluate the response of 50 Kabuli chickpea genotypes via a randomized complete block layout with three replicates.The entry by tester (genotype by trait) biplot which explained 66% of the variability indicated that the important traits for a favorable genotype in semiarid environments were seeds' number of pod and pods' number of single plant.The biplot model introduced some desirable chickpea genotypes as good for a trait or a category of traits; genotype 27 for chlorophyl content, genotype 26 for seed yield (SY), SP and PP, and genotype 36 for plant height (PH), days to maturity (DM), pod's weight (PW), hundred seed weight (HSW), plant dry weight (PDW), and plant fresh weight (PFW).Based on an ideal assumptive genotype (entry) position, genotype 1 followed to 2, 10, 16, 17, 23, 26, 33 and 34 were ideal regarding the distinction ability and typical potential.According to an ideal assumptive trait (tester) position, PH, PDW, and PFW were more discriminative and typical traits.The responses of chickpea genotypes regarding SY indicated that genotype 26 following to 3, 17 and 27, were the most desirable and can be advised for commercial cultivar release process.
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.
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 | 0.000 |
| 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 it