Variation of Clumping Index With Zenith Angle for Forest Canopies
Bibliographic record
Abstract
Canopy clumping index (CI) characterizes the extent of the nonrandom spatial distribution of foliage elements within a canopy and is critical for determining the radiative transfer, photosynthesis, and transpiration processes in the canopy. It is widely perceived that CI increases with zenith angle (θ), because between-crown gaps decrease in size and number with increasing θ. In this study, we demonstrate that this is not always true. Analytical equations between CI and θ are first developed based on widely-used forest canopy gap fraction theories. The results show that the zenith angular variation of CI is closely related to crown projected area or crown shapes (i.e., the ratio of the crown height to its diameter, RHD): CI increases with θ for canopies with “tower” crowns (RHD > 1), but decreases with θ for “umbrella” crowns (RHDin-situmeasurements and multi-angular remote sensing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".