10.51847/Ai6pbgFRyJ
Bibliographic record
Abstract
Pakistan is spending a large amount of foreign exchange on the import of huge quantity of edible oil which is snowballing every year due to increase in demand and population.The present study was conducted to study the relationship between different yield related traits in advance lines of Brassica juncea developed through pedigree method of plant breeding.The yield performance of these advance lines was also evaluated during the experiment.Twelve lines were grown in Randomized Complete Block Design with three replications during winter 2015-16.All the recommended cultural practices were used throughout the experiment.The data for plant height, days to flowering, days to maturity, branches per plant, seeds per silique, seed yield per plot and oil% was collected.The correlation analysis revealed that seed yield per plot was highly and significantly correlated with number of branches per plant and seeds per siliqua.RBJ-11008 and RBJ-12018 were produced significantly higher seed yield (2.21kg/plot) & (2.10kg/plot) respectively than the check variety Khanpur Raya.The present study has clearly indicated the need for giving due weightage for number seeds per siliqua and number of branches per plant for improving seed yield in Mustard.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.955 | 0.958 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".