Study on the Characteristics of Seed Dormancy and Germination of Gynostemma guangxiense X.X.Chen et D.H.Qin
Bibliographic record
Abstract
Seed viability test,seed germination test,water-absorbing test,isolated embryos gennination test of Gynostemma guangxiense X.X.Chen et D.H.Qin were carried out,and seeds of G.guangxiense were dealed with 98%H_2SO4,different solution concentration of NaOH,low temperature stratification treatment,dry storage treatment,different solution concentration treatment of GA_3 and 6-BA,so as to discuss the influence of different treatment on seeds germination of G.guangxiense.The results showed that the fresh seeds viability of G.guangxiens was 98%,however seeds gennination of it was just 11.7%when they were cultivated under 5 constant temperature and one alternating temperature,which indicated that there was obvious dormancy phenomenon on fresh seeds of G.guangxiens.Seed coat of G.guangxiens inhibited water absorbtion of seeds, and isolated embryos germination test indicated that in-vitro embryo of seeds haven't undergo dormancy,which suggested that seed coats were the main cause that inhibited the seed germination.The tests of breaking seed dormancy showed that dormancy of seeds could not be broken by both low temperature stratification and dry storage method,and the treatment of 98%H_2SO_4 and different solution concentration of NaOH.Only the treatment of 6-BA with 4℃stratification could heighten the germination rate of G.guangxiens seeds,but the effect of promotion was very poor.
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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".