Effects of Litter and Seed Position on Seedling Establishment of Gentiana dahirica Fischer
Bibliographic record
Abstract
In recent three decades the medicinal plant Gentiana dahurica Fischer has been threatened by large scale exploitation for trade and land-use practices, such as over-grazing and reclamation. However, research into the population ecology and sustainable use of this highly-threatened medicinal plant is lacking, and so we analyzed the effects of different litter applications and seed positioning (surface-sown or buried) as part of a program to rehabilitate it. The results indicated that although litter and seed position per se had no significant effects on the seedling emergence of G. dahurica, interestingly, litter application did show positive effects on seedling survival, e.g. 100 g m-2 and 200 g m-2 litter increased it by 9.7% and 16.4%, respectively, compared to control. Moreover, the seedling leaf area increased with the quantity of applied litter when seeds were surface-sown, with the highest leaf area occurring in the 200 g m-2 treatment (9.7-fold of the control), and litter-covered treatments also significantly improved seedling root length and root diameter of G. dahurica compared to controls. Taken together, these data indicated that seedling establishment of G. dahurica benefited from accumulated litter, and it is thus suggested that fencing to exclude grazing for a period is the best method to protect and rehabilitate the threatened wild G. dahurica populations.
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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".