Foliage height influences specific leaf area of three conifer species
Bibliographic record
Abstract
Specific leaf area (SLA), the ratio of projected leaf area to leaf dry mass, is a critical parameter in many forest process models. SLA describes the efficiency with which the leaf captures light relative to the biomass invested in the leaf. It increases from top to bottom of a canopy, but it is unclear why. We sampled stands with low and elevated canopies (young and old stands) to determine whether SLA is related to water potential, as inferred from branch height and length, or shade, as inferred from branch position relative to the rest of the canopy, or both. We studied western white pine (Pinus monticola Dougl. ex D. Don), ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.), and interior Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) in northern Idaho. SLA decreased with branch height (P < 0.0001) at rates that varied among species (P < 0.0001). Branch length had no influence on SLA (P = 0.85). We detected no differences with canopy elevation (P = 0.90), but the slopes of lines relating SLA to branch height may have differed between the canopy elevation classes (P = 0.039). The results are consistent with predictions based on the hypothesis that SLA decreases as the gravitational component of water potential falls. The lack of a strong shading effect simplifies the estimation of canopy SLA for process models, requiring only species and branch heights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".