Stability Analysis for Appearance Qualities of Rice Cultivar and Genotype × Environment Interaction and Its Relationship to Climate Factors.
Bibliographic record
Abstract
Based on AMMI model,studied 25 rice varieties appearance quality character and the interaction with environment,and probed into the cultivar stabilities,correlation between G×E interaction and climate factors,Eco-climatic adaptability in chalkiness qualities.The results showed that G×E interaction in grain length(GL),grain width(GW) and ratio of grain length to width(RLW) were no different significantly;and G×E interaction in chalky grain rate(CR) and chalkiness degree(CD) were significant at 1% level.The performance of cultivar was divided into three sorts that with best stability and better chalkiness character,moderate stability and best chalkiness character and worst either stability or chalkiness character.Correlation analysis between chalkiness character's iPCA 1 and air temperature,sunlight,rainfall within paddy growth period indicated that climate factors were the most important environment factor which can decide G×E interaction.Accordingly the best quality adaptability of different cultivars to climate factors could be estimated.For instance,V 9 and V 24(Shennong 9819 and 0142) had especial adaptability to lower temperature in the chalky grain rate,V 23(Yanyou 5858) had especial adaptability to scant sunlight and higher temperature in the chalkiness degree.In actual rice plant,we may obtain lower chalky-grain rate and chalkiness degree through selecting some planting sites with reverse interaction to the variety.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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".