Relationships between shoot size and branching patterns in 10 broad-leaved tall tree species in a Japanese cool-temperate forest
Bibliographic record
Abstract
Within-tree variations in branching patterns (the patterns of daughter shoot production by mother shoots) are the basis of tree architectural plasticity and, therefore, were studied in 10 cool-temperate broad-leaved tall tree species including three species with distinct short shoots. The relationships between mother shoot length versus branching patterns (i.e., the number and size of daughter shoots) were quantified for each species using regression equations. The number and stem length of daughter shoots were greater on longer mother shoots, although the majority of daughter shoots were short on mother shoots of any size. The magnitude of lateral spread of the mother shoot – daughter shoot system relative to that of its main axis extension increased with increasing mother shoot length, indicating weaker apical control on longer mother shoots. Among species, the lower limit of daughter shoot length was shorter and the frequency of short daughter shoots was greater on mother shoots of species with more distinct short shoots. This indicates that species with distinct short shoots effectively avoid branch overcrowding by minimizing daughter shoot extension. Differences in branching patterns among mother shoots of different sizes are discussed in relation to their functional importance for tree architectural development.
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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".