Multivariate Genetic Analysis of Main Characters in Progenies of Transgenic Rice Mediated by Biolistic Bombardment
Bibliographic record
Abstract
Characters of growth period,yield-related and resistance to sheath blight on T_(4) transgenic progenies of E32 mediated by biolistic bombardment was investigated by multivariable heriditary analysis.The results indicated twelve indexes were divided into five common factors by factor analysis and the cumulative variance contribution of five factors reached 92.44%.Total grains per panicle(TGP),grains/cm(G/cm),filled grains per panicle(FGP) played important roles in the first factor.Disease index(DI),disease grade(DG) and disease stalk rate(DR) owned higher factor loading values in the second factor. The third factor was mainly dominated by days before heading(DH) and period of grow(DG).Only number of panicles(NP) and seed setting rate(SSR) played primary roles in the fourth factor and five factor respectively.Twenty transgenic progenies of(E32) were divided into four types by clustering analysis according to the scores after promax rotation,varieties of different type had different characters of grow period,resistance to sheath blight and yield.Therefore,the improvement procedure can be an efficient way for rice breeding according to the relative importance of these principal factors and characters of different type varieties.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".