Analysing species-specific light transmission and related crown characteristics of<i>Pinus sylvestris</i>and<i>Betula pendula</i>using a shoot-level 3D model
Bibliographic record
Abstract
This is a first attempt to analyse species-specific light attenuation in mixed boreal forests created by shoot-level 3D tree models. The models are configurations of real individual Scots pine (Pinus sylvestris L.) and silver birch (Betula pendula Roth.) trees grown in mixed-forest stands. We study species-specific differences in radiation transmission by using the ray-casting method. Light transmission was found to be higher in dense birch-dominated stands compared with dense pine-dominated stands because of the higher total foliage area and the higher location of foliage in the pine canopy. Transmission of light per leaf area index (LAI) was nevertheless higher in the pine canopy compared with the birch canopy because of foliage clumping. Especially in clumped canopy, species-specific shoot-level light simulations enable a more realistic estimation of light transmission compared with simpler calculations based on LAI and Beer–Lambert’s law. The observed differences in light-transmission characteristics of the studied species may influence the development of target crowns in the neighbourhood. Light attenuation caused by the dense foliage zone in the upper pine canopy could be avoided in dense stands by mixing species with different vertical foliage distributions.
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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".