Shoot population recruitment from a bud bank over two seasons of undisturbed growth of <i>Leymus chinensis</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it