MVP: a model to simulate the spatial patterns of photosynthetically active radiation under discrete forest canopies
Bibliographic record
Abstract
The spatial patterns of photosynthetically active radiation (PAR) under forest canopies, including both its mean and spatial variation, are critical factors to numerous understory ecophysiological processes. Currently, Beer's law is the primary algorithm used in ecological models simulating PAR transmission through plant canopies, because more accurate models are too complicated to be used operationally. This study developed a simple and computationally efficient model at a stand scale to simulate both the mean and variation of PAR (MVP) under forest canopies. The model assumes that a forest canopy is composed of individual crowns distributed within upper and lower boundaries with two types of gaps: the between- and within-crown gaps. The between-crown gaps are simulated with geometric optics, and the within-crown gaps are described by Beer's law. The model accounts for the covariance of PAR in space through time, making it possible to simulate both instantaneous and daily accumulated variance of PAR. Validation with observed PAR from the boreal ecosystematmosphere study (BOREAS) indicates that the model captures the mean and variance of PAR under forest canopy reasonably well. MVP holds the potential to improve simulation of light interception by forest canopies as well as the treatment of canopy rainfall interception in ecological models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".