Comparing PAR transmission models for forest understorey vegetation
Bibliographic record
Abstract
Abstract Question: Do Beer's Law models, multi‐layer scattering models, and a semi‐empirical model for predicting PAR transmission through understorey vegetation give comparable results? Do different driving variables (LAI, PLAI and percentage cover) give different results? How do the models vary when fit with species‐specific, species‐average and the ‘default’ parameters recommended in the literature? Location: Upland boreal forests of western North America. Methods: In calibration and validation plots, PAR transmission was measured, total cover visually estimated, and leaf dispersion, PLAI and cover estimated for each species using a point‐frame. Leaf inclination was measured by clinometer. PAR transmission was modelled using empirically‐fit Beer's Law models, a semi‐empirical model based on hemispherical gap fraction and first‐order scattering, and a multi‐layer model allowing multiple scattering. All models were modified to use leaf area index (LAI), vertically projected leaf area index (PLAI), or percentage cover data. Results: The empirical Beer's Law models had the least bias and best precision in predicting PAR transmission. The semi‐empirical model also had little bias and good precision, since the scattering coefficient compensated for problems in the estimation of gap fraction. The multi‐layer model consistently underestimated transmission. There was little benefit in accounting for species separately. LAI and PLAI‐based models were the most precise, but percentage cover models also provided reasonable predictions of PAR transmission. Conclusions: PAR transmission through forest understories can be simply modelled with Beer's Law using one empirical coefficient representing the average understorey species. More complex scattering models are less effective, likely because they fail to account for the complexity of the dispersion of this vegetation layer and its effect on radiation scattering.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".