Bibliographic record
Abstract
A plant species' ecology is associated with leaf economics, characterized, e.g., by photosynthetic rate, construction costs, and leaf life span. Specific leaf area (SLA, leaf area per leaf dry mass) is often considered to be a key trait in this respect, explaining interspecific variation in leaf economics. To understand factors constraining the specific leaf area, we investigated size-related biomechanical constraints of the traits that determine the SLA-leaf thickness, leaf dry matter content and leaf density-and the constraints these traits exert on each other among 33 herbaceous wetland species of northern Ontario with a wide variety of leaf forms, ranging from wide laminar leaves to long, narrow, and relatively thick columnar leaves. Data from garden experiments were compared with field data. The results agree with biomechanical predictions that lamina thickness and leaf dry matter content are positively and leaf density (fresh mass per volume) negatively associated with leaf length. The traits also constrain each other, but these intertrait relationships are confounded by interspecific variation in leaf length. We conclude that for a full understanding of the adaptive significance of leaf structural design, it is essential to include leaf size in the considerations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".