Functional specialization of current shoots as a reproductive strategy in Japanese alder (<i>Alnus hirsuta </i>var. <i>sibirica</i>)
Bibliographic record
Abstract
Current shoots, which form the crown of a tree, are specialized in various functions such as crown expansion, reproduction, and assimilation. We examined the temporal and spatial distribution of reproductive shoots in Alnus hirsuta Turcz. var. sibirica (Fischer) C.K. Schn., assessed their direct and indirect costs of reproduction, and explained their distribution in the crown as the reproductive strategy of a current shoot population. The upper and lower limits to the lengths of current shoots for reproductive growth (flower formation) were 40 and 10 cm, respectively. Reproductive 1-year-old shoots produced fewer shoots in the following year than non-reproductive 1-year-old shoots. In current shoots longer than 40 cm, the increment of reproductive output in the following year by abandonment of reproduction surpassed the decrement of reproductive output in the current year by abandonment of reproduction. This may be one reason for the upper limit of reproductive shoot length. Thus, the current shoot population of A. hirsuta var. sibirica may be divided into three functionally specialized subpopulations: reproductive, maintenance, and exploratory. This specialization is considered to be a reproductive strategy to maximize their lifetime reproductive success.Key words: current shoot population, reproductive ecology, functional specialization, cost of reproduction, Japanese alder.
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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 | 0.001 | 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".