Study on the Optimum Condition of New and Old Seed Germination of Dioscorea nipponica Makino
Bibliographic record
Abstract
Objective:The author studied the optimization of GA3、NAA and cold sandy stratification treatment on new and old seeds germination of Dioscorea nipponica Makino.Methods:The research used the method of culture,and the best condition that was selected from 3 kinds of treatments on old seeds.Results The result showed that the D.nipponica new seeds just treated with cold sandy stratification(4 ℃) germinated best at 25 ℃,and the germination rate and germination energy were 80% and 53.33%,and the old seeds' were 45% and 25% respectively.The new seeds treated with 100 mg/L GA3 for 24 hours germinated the best,and the germination rate and germination energy were 66.65% and 48.35%,and the old seeds' were 40% and 25% respectively.The new seeds treated with 1 mg/L NAA for 12 hours germinated the best and the germination rate and germination energy were 58.35% and 43.35%,and the old seeds' were 30% and 23.35% respectively.The new seeds treated with 100 mg/L GA3 for 24 hours began to germinate on the 10th day and finished on the 22 nd day,and the new seeds treated with 1 mg/L NAA for 12 hours began to germinate on the 11 th day and finished on the 24th day.The new seeds treated with cold sandy stratification(4 ℃) began to germinate on the 6th day and finished on the 12th day.Conclusion:If cold sandy stratification would be available in production process,it could be recommended to treat D.nipponica new seeds with cold sandy stratification(4 ℃) for 60 days and then accelerate germina at 25 ℃.If not,treat D.nipponica new seeds with 100 mg/L GA3 for 24 hours directly.
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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 | 0.000 |
| 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".