Research on the Seed's Germination Characteristic of Zenia insignis as the Vanguard Tree for Afforestation in Karst Areas
Bibliographic record
Abstract
In this experiment,the Zenia insignis seeds were dealt with five methods including rubbing with fine sand and sandpaper,caustic soda solution immersion,hot water immersion,sulfuric acid immersion and aqua fortis immersion,the datas which obtained from different treatments were analysed.The results show that treatment by rubbing with fine sand and sandpaper not only can improve the permeability of the seed shell obviously and enhance seed's germination percentage and germination tendency,but also the operation is easy,the equipment is simple,without pollution,it is a method worth considered.The seed's water permeability was improved obviously by treatment with different density sulfuric acid immersion.Especially in treatment by strong sulfuric acid for 30min,the seed's germination percentage reach as high as 99.0%.But there is troublesome and environment risk with handling the abandoning sulfuric acid,the cost is high.The seed's germination percentage is also higher of the treatment by soaking seeds with hot water at 80℃,may reach 75.0%.There is no remarkable change of seed's germination percentage of the treatment by caustic soda solution immersion with density at 30%.The seed's germination percentage increases gradually along with time extension of the treatment by aqua fortis immersion,the seed's germination percentage is the highest by soaking seeds for 30min,may reach 45.0%.Considering on comprehensive factors such as cost,technical difficulty and environment risk and so on,we think the treatment by soaking seeds with hot water is the best method.
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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 | 0.001 | 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".