Shoot population recruitment from a bud bank over two seasons of undisturbed growth of <i>Leymus chinensis</i>
Bibliographic record
Abstract
Shoots of many clonal species are iterated seasonally by programmed growth from bud banks. Buds are not all the same but differ in their positions; these positions determine their densities, trigger times, and seasonal dynamics. To determine how different bud types contribute to a shoot population in an undisturbed environment, the densities of each bud type and daughter-shoot type were investigated in Leymus chinensis (Trin.) Tzvelev. New horizontal rhizomes (A1-1) start to grow in late May, but new vertical buds (including the vertical apical rhizome buds (A1-2), axillary rhizome buds (B1), and axillary shoot buds (C1)) emerge in late June; after late June or late July, the density of A1-1 gradually decreased, whereas the vertical buds increased. In mid-season, the presence of a high proportion of A1-1, suggests that plants pursue a spreading strategy. Late in the season, a high proportion of vertical buds suggests that they adopt a propagation strategy. At the end of the growing season, the stable contributions of type-specific buds (A1-2, 16%; B1, 5%; C1, 79%) to the overall shoot population may explain the dominance of this species throughout the eastern Eurasian Steppe. Developing a clearer understanding of bud dynamics and their type-specific contributions under undisturbed conditions, is a necessary prerequisite for predicting their responses under disturbed conditions.
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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".