A model to estimate light interception by tree crowns, applied to black spruce
Bibliographic record
Abstract
CORONA is a spatially explicit model designed to efficiently estimate the quantity of shortwave radiation intercepted by individual tree crowns. Radiation interception is determined from intersections of light rays with tree crowns for rays originating throughout the sky hemisphere. CORONA comprises a flexible, asymmetric description of crowns, a computationally efficient ray-tracing procedure, numerical solution of ray × crown intersections, and several alternative within-crown light interception functions. The interception functions were calibrated and evaluated in two plots located in black spruce (Picea mariana (Mill.) BSP) stands, where tree positions and crowns were mapped. An interception function using only the number of intercepting crowns (i.e., a hits function) generally resulted in better agreement between modelled and observed light levels than an interception function using the total path length through crowns. There was some evidence that transmission was greater in parts of crowns within low-light-level environments. This effect can be incorporated into an interception function for more realistic modelling of interception. Errors in crown description are likely the main cause of discrepancies between modelled and observed light levels on a half-hourly basis. The agreement between modelled and observed light levels on a daily basis indicates that CORONA can provide useful estimates of light interception by tree crowns.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".