Effects of Temperature and Gibberellin Treatment on Seed Germination Characteristics of <i>Caryopteris incana</i> (Thunb.) Miq.
Bibliographic record
Abstract
Caryopteris incana (Thunb.) Miq . is an excellent ornamental, nectariferous plant and medicinal plant as a widely distributed species of the Caryopteris . Based on the determination of the length, width, 1 000-grain weight and water absorption of vanilla seeds, the effects of temperature and gibberellin treatment on seed germination were explored. The results showed that the average length and width of the seeds were 2.43 mm and 1.94 mm. The 1000-grain weight of the seeds was 0.57 g. The seed reached saturation after 4 hours of water absorption. Seeds can germinate at 15℃~30℃ and hardly germinated above 35℃. The optimal temperature for germination is 20℃~25℃. Gibberellin treatment can significantly improve the germination rate of Cyperus angustifolia , especially under the conditions of low temperature (15℃) and high temperature (35℃), 100 ~ 200 mg/L gibberellin treatment can improve the germination rate and shorten the germination period of the seeds. This study provides important theoretical support for the sowing and propagation of C. incana , and the theoretical basis for the planting and breed improvement of C. incana in the future.
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 | 0.001 |
| 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".