Do spatial variation in leaf traits and herbivory within a canopy respond to selective cutting and fertilization?
Bibliographic record
Abstract
Within a canopy, spatial variation in leaf traits may be determined by light and nutrient availabilities. Such environmentally caused changes in leaf traits may be an important cause of changes in leaf palatability to herbivores. We conducted a factorial experiment with fertilization and selective cutting in a northern Japanese forest dominated by oak ( Quercus crispula Blume). Fertilization increased the nitrogen content of upper canopy leaves. Leaf mass per area (LMA) was greater in the upper canopy than in the lower canopy. Selective cutting and all interactions had significant effects on LMA. Total phenolics and condensed tannin in leaves were also greater in the upper canopy than in the lower canopy. The interaction of selective cutting × position in the canopy (upper or lower) had a significant effect on total phenolics; a similar trend was seen for condensed tannin. Herbivory was greater in the lower canopy than in the upper canopy. Also, fertilization increased herbivory, whereas selective cutting decreased it. These results imply that human activities, such as logging and nitrogen deposition, may strongly influence spatial variation in herbivory through changes in leaf traits.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".