Morphological and physiological responses of beech and oak seedlings to canopy conditions: why does beech dominate the understory of unmanaged oak fuelwood stands?
Bibliographic record
Abstract
Secondary forest succession after abandonment of traditional agricultural and silvicultural lands is occurring in many parts of the world. Quercus crispula Blume– Quercus serrata Murray fuelwood stands have so far been artificially maintained in natural Fagus crenata Blume forests, central Japan, by resprouting after cutting. It is known that unmanaged fuelwood stands return to F. crenata dominated stands. This study aimed to examine why the two Quercus species are replaced by F. crenata by investigating seedling morphological and physiological responses of the three species to canopy conditions (forest edge and understory). The two Quercus species allocated more carbon to roots and stored more total nonstructural carbohydrates in roots as compared with F. crenata. Therefore, the two Quercus species allocate more photosynthetic production to roots than aboveground growth to maintain sprouting ability. On the contrary, F. crenata increased the light-harvesting efficiency (i.e., low leaf mass per area and high chlorophyll concentration) in the understory and increased height growth at the forest edge by greater allocation to stem. These traits would be beneficial for an increase in survival in the understory and height growth at the forest edge. Therefore, it is suggested that Quercus species in unmanaged stands will be replaced by F. crenata, a competitively superior species.
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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) | 0.000 | 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".