Specific Leaf Area and Dry Matter Content Estimate Thickness in Laminar Leaves
Bibliographic record
Abstract
BACKGROUND AND AIMS: Leaf thickness plays an important role in leaf and plant functioning, and relates to a species' strategy of resource acquisition and use. As such, it has been widely used for screening purposes in crop science and community ecology. However, since its measurement is not straightforward, a number of estimates have been proposed. Here, the validity of the (SLA x LDMC)(-1) product is tested to estimate leaf thickness, where SLA is the specific leaf area (leaf area/dry mass) and LDMC is the leaf dry matter content (leaf dry mass/fresh mass). SLA and LDMC are two leaf traits that are both more easily measurable and often reported in the literature. METHODS: The relationship between leaf thickness (LT) and (SLA x LDMC)(-1) was tested in two analyses of covariance using 11 datasets (three original and eight published) for a total number of 1039 data points, corresponding to a wide range of growth forms growing in contrasted environments in four continents. KEY RESULTS AND CONCLUSIONS: The overall slope and intercept of the relationship were not significantly different from one and zero, respectively, and the residual standard error was 0.11. Only two of the eight datasets displayed a significant difference in the intercepts, and the only significant difference among the most represented growth forms was for trees. LT can therefore be estimated by (SLA x LDMC)(-1), allowing leaf thickness to be derived from easily and widely measured 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".