Light absorption and bole volume growth of individual Douglas-fir trees
Bibliographic record
Abstract
Empirical growth and yield models for forest management are evolving toward individual-tree models that are capable of simulating the growth of mixed and uneven-aged stands. Spatially explicit (i.e., distance-dependent) models usually modify the growth of trees by means of competition indices; however, these competition indices rarely simulate the light available for tree growth explicitly. We used tree growth data from an even-aged, unthinned, 50-year-old Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stand in British Columbia to test the hypothesis that the amount of absorbed light is a good predictor of diameter at breast height, height, and bole volume growth of an individual tree. We also explored the relationships between these variables. A spatially explicit light model was used to simulate photosynthetically active radiation absorbed by individual trees during a growth period (APAR) based on detailed canopy architecture information. For the purpose, we used a weighted leaf area (WLA) that is linearly related to APAR. Because of the integration of light absorption by a tree crown, estimates of WLA were highly correlated with leaf area for dominant trees. For suppressed trees, leaf area was a poor estimator of WLA. The relationship between WLA and bole volume growth was nonlinear, indicating a higher light-use efficiency in suppressed trees than in dominant trees. This relationship was strong enough to be useful for growth modeling. Only height growth of suppressed trees was affected by WLA. We conclude that single-tree WLA can be used as a process-oriented competition index in growth models for forest management.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".