The suitability of leaves from different canopy layers for a generalist herbivore (Lepidoptera: Lasiocampidae) foraging on sugar maple
Bibliographic record
Abstract
Variations of leaf suitability within forest canopies may have important consequences for the biology of phytophagous insects. In this study we examined over 4 consecutive years (19941997), the influence of vertical stratification of leaves within a sugar maple (Acer saccharum Marsh.) stand on biological performance and feeding preference of Malacosoma disstria Hbn. Each year, 10 healthy sugar maple trees and about 15 understory sugar maple seedlings were selected. Leaves were collected from the lower (36 m above ground) and the upper crown (2025 m above ground) sections of the trees and from seedlings. Sampled leaves were set in Petri dishes for insect rearings in controlled environment. The performance of the insect, especially pupal masses and the number of eggs of adult females, was higher when larvae were fed with leaves from the upper crown section of trees. Results for the feeding preference tests showed that larvae of fourth instars consumed more surface area from leaves collected in the upper crown section of the trees. More total nitrogen found in leaves from the upper tree crown could explain the higher performance of this insect. Our results confirm the importance of the heterogeneity in leaf suitability along a vertical stratification in forests by its influence on biological performance and feeding preference of M. disstria.
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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".