Does shade improve light interception efficiency? A comparison among seedlings from shade‐tolerant and ‐intolerant temperate deciduous tree species
Bibliographic record
Abstract
Here, we tested two hypotheses: shading increases light interception efficiency (LIE) of broadleaved tree seedlings, and shade-tolerant species exhibit larger LIEs than do shade-intolerant ones. The impact of seedling size was taken into account to detect potential size-independent effects on LIE. LIE was defined as the ratio of mean light intercepted by leaves to light intercepted by a horizontal surface of equal area. Seedlings from five species differing in shade tolerance (Acer saccharum, Betula alleghaniensis, A. pseudoplatanus, B. pendula, Fagus sylvatica) were grown under neutral shading nets providing 36, 16 and 4% of external irradiance. Seedlings (1- and 2-year-old) were three-dimensionally digitized, allowing calculation of LIE. Shading induced dramatic reduction in total leaf area, which was lowest in shade-tolerant species in all irradiance regimes. Irradiance reduced LIE through increasing leaf overlap with increasing leaf area. There was very little evidence of significant size-independent plasticity of LIE. No relationship was found between the known shade tolerance of species and LIE at equivalent size and irradiance.
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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) | 0.001 | 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".