Effects of Endophyte-infected Leymus chinensis on Seed Germination of Stipa krylovii
Bibliographic record
Abstract
Leymus chinensis and Stipa krylovii,two dominant species in the Inner Mongolia Steppe,were used as the experimental materials.The aim of this study was to test whether endophyte infection of L.chinensis would influence seed germination of S.krylovii when treated with organs in vitro or the whole plant.The results showed that endophyte infection could change the effects of host plant L.chinensis on seed germination of S.krylovii,the effect was related to grass organs,treatment methods and concentrations.Compared with endophyte-free(E-) leaves,endophyte-infected(E+) green leaves significantly increased seed germination rate of S.krylovii in lower concentration while decreased radicle growth of S.krylovii in higher concentration.E+ dead leaves could reduce radicle growth of S.krylovii more than E-leaves while E+ rhizome increased the germination rate more than E-leaves.As for the whole plant,endophyte infection had no significant effect on seed germination of S.krylovii.When treated with the same concentration,grass powder exhibited a stronger reduction in seed germination and seedling growth than water extract,and green leaves exhibited a stronger reduction in seed germination and seedling growth than dead leaves.
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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".