Effects on Germination Characteristics with Different Treatments for Seed of Platycodon grandiflorum( Jacq. ) A. DC
Bibliographic record
Abstract
To improve the germination rate and provide a scientific basis for artificial cultivation of Platycodon grandiflorum( Jacq.) A. DC,the physiological indexes of P. grandiflorum like the length and the width of the seeds,the seed purity and 1 000-grain weight were measured. The germination rates were determined under the treatments of different frozen time,soaking in different temperatures water,different germination bed and soaking in different pH solutions. The seed germination rate could reach 72. 33% under the treatment of frozen5 d Different soaking temperature treatments had different influence on seed germination rate,it was the highest whea soaking in 40 ℃ water reached 65%; The suitable germinating bed was beneficial to the seed germination,and the highest rate was 70. 33% on the double layer guaze; The highest germination rete reached65. 33% when the seeds were soaked in the solution( pH = 7).
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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) | 0.001 | 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".