Branch growth and allocation patterns of saplings of two Abies species under different canopy conditions in a subalpine old-growth forest in central Japan
Bibliographic record
Abstract
Branch growth and biomass allocation of saplings of two Abies species under three different canopy conditions (evergreen coniferous Abies canopy, deciduous broad-leaved Betula canopy, and canopy gaps) were examined in a subalpine old-growth forest in the northern Yatsugatake Mountains, central Japan. Both A. mariesii and A. veitchii saplings showed similar morphological plasticity: saplings growing in gaps had conical crowns, whereas those under evergreen and deciduous canopies had umbrella-shaped crowns. Both Abies species grew more in stem diameter than in stem height under evergreen and deciduous canopies. Under evergreen and deciduous canopies, A. veitchii saplings invested more biomass in stems and less in leaves and branches than did A. mariesii. The mean longevity of leaves and branches of A. veitchii saplings was shorter than that of A. mariesii saplings under evergreen and deciduous canopies. These results suggest that A. veitchii saplings have an efficient umbrella-shaped crown for light interception and invest relatively little biomass in leaves and branches compared with A. mariesii saplings in shaded conditions. However, branch mortality of A. veitchii saplings may be higher, because the species’ high leaf area ratio and specific leaf area may increase its susceptibility to herbivory and disease and also because the slender branches of A. veitchii saplings would be more likely to break under the weight of snow. The growth strategy of A. mariesii in shade is considered persistent, while that of A. veitchii saplings emphasizes height growth.
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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) | 0.000 | 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 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".