Inclusion of the Fractal Dimension of Leafless Plant Structure in the Beer‐Lambert Law
Bibliographic record
Abstract
The Beer‐Lambert law is commonly used to describe the relationship between the proportion of light penetrating a plant canopy and the leaf area index (LAI). Although the geometric distribution of leaf area has a potential effect on the ability of a plant to intercept light, the equation contains no term to account for it. In this study, the geometric distribution of leaf area was quantified by the fractal dimension of leafless plant structure (FD). The objective was to evaluate the contribution of plant structure complexity to the Beer‐Lambert law, by including FD in the equation. The crop was soybean [Glycine max. (L.) Merr.]. Data were collected according to a block design with four blocks and five weekly repeated measures. The analyzed variables were LAI and light penetration (% per plant), and FD, estimated using leafless plants photographed from the side that allowed the maximum appearance of branches and petioles. Statistical analyses were performed week by week, on weekly means and on block means. When LAI and FD were significantly correlated (i.e., at the end of canopy development and on weekly means), inclusion of either variable as regressor in the equation provided similar goodness‐of‐fit. In other instances, inclusion of FD as a multiplicative factor of LAI increased the r2 value up to 0.31. In all instances, the correlation between light penetration and FD was stronger than between light penetration and LAI. In summary, the application of the Beer‐Lambert law for light penetration into the canopy is improved by inclusion of FD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".