Bibliographic record
Abstract
Objective: To explore the optimum condition on germination of Ilex hainanensis Merr. in order to provide guidance for seedling breeding,we studied the germination characters. Methods: Shapes,weights per thousand seeds and content of moisture were observed. The germination rates of the seeds were determined under different soaking time,different illumination and temperature treatments,and the correlation among them were measured. The difference of germination rate,germination index,numbers of initial germination and germination days were also compared after storage for a period of time. Results: The mean length and width of seeds were 2. 84 mm,1. 11 mm,respectively. Length to width ratio 2. 50. weights per thousand seeds was0. 716 9 g and content of moisture was 12. 36%. Seeds under soaking time for 24 hours was gained the highest germination rate when other conditions were the same. Germination rate under illumination was significantly higher than those under dark. With the increase of temperature,the germination rate raise firstly and then fell,and 30 ℃ was optimum. Different storage time had influence on germination rate,while stored for three months at 4 ℃ obtained the highest rate(96. 33%) and index(13. 78),the maximum numbers of initial germinationand(24 seeds) the shortest germination days(28 days). Conclusion: Stored for three months at 4 ℃,soaking time for 24 hours,30 ℃,illumination were the optimum condition for seeds' germination of Ilex hainanensis Merr.
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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".