Morphological and physicochemical traits of leaves of different life-forms of various broadleaf woody plants in interior Alaska
Bibliographic record
Abstract
The morphological and physicochemical traits of leaves are important in terms of plant adaptation to various growth environments, because such traits play central roles in various functions including photosynthesis. We measured the toughness, mass per unit area (LMA), nitrogen content, and δ15N levels of the leaves of different life-forms of 39 broadleaf woody plants in interior Alaska. The plants were divided into three life-forms based on the maximum height of adult plants: understory (<1 m), small (≥1 m to <5 m), and canopy (≥5 m). Evergreen species accounted for a large proportion of understory woody plants (44%), whereas most small (87%) and canopy (100%) trees were deciduous. Higher LMA, increased toughness, and higher C-to-N ratios prolonged leaf life-span in evergreen species. We also evaluated the presence or absence of bundle-sheath extensions (BSEs), which contribute to the mechanical support of leaves and enhance photosynthetic ability by improving hydraulic properties. In total, 11 species (28%) had homobaric leaves (BSEs were absent) and 28 species (72%) had heterobaric leaves; these proportions are similar to those of woody plants of other temperate deciduous forests. The δ15N values reflected the presence of root symbionts such as ericoid mycorrhiza or Frankia sp. Our results suggest that leaf traits may affect both the life-form of the host plants and leaf longevity in both evergreen and deciduous 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".