Effect of the Different Chemistry Mutagens on the Germination and Growth of Rice Seed
Bibliographic record
Abstract
Uses different type chemistry mutagens treated the paddy rice seed, The results showed that:with chemistry mutagen processing density increase, The paddy rice seed's germination viability, the germination percentage, the vigor index, root and bud the growth condition and so on basically have a decreasing trend. But low density EMS(0.5%), MNU(0.05%) has enhanced the function which to rice seed germination percentages, the germination tendency, the vigor index as well as the promotion late rice seed root and the bud grows;High concentration EMS(2.0%), NaN3 (2×10-3mol/L)reduce the germination percentage and the vigor index of seed. And high concentration chemistry mutagens that 2.0%EMS and 2×10-3mol/L NaN3 has more inhibitory effect on bud growth of rice than on its root. The results compared with contrast showed that root longness and seedling high of growth fluctuation was early rice more wide than the late rice after treated with chemical mutagens. and shows early rice more sensitive than the late rice. When EMS and the NaN3 density is higher separately than 1.0×10-3mol/L, 2.0%, above ground (stem and leaf) and under-ground (root) of the paddy rice seedling are suppressed, The exhibition is that the root longness and the seedling high become shortens(except R974), And reaches the significance level compared with contrast.
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 | 0.000 | 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) | 0.001 | 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".