Predicting survival and growth rates for individual loblolly pine trees from light capture estimates
Bibliographic record
Abstract
Light capture estimates from models can be related to survival and growth rates and may provide new ways to model forest dynamics. However, relationships between light capture, growth, and survival should vary widely with tree age, site conditions, and stand density, so predictions from light capture models need to be tested over a range of stand conditions. We used the tRAYci stand light model (A. Brunner. 1998. For. Ecol. Manage. 107: 1946) to estimate weighted leaf area (WLA), an estimate of annual light capture, for every tree, in 36 even-aged loblolly pine (Pinus taeda L.) stands, representing different combinations of site index and planting density, over an 8-year period. We also developed regression equations relating light capture estimates to height growth, basal area growth, stem volume growth, and survival probability for individual trees at different ages, sites, and planting densities. Our results suggest a significant correlation between estimates of WLA and tree growth and survival rates, and that the tRAYci model is robust across a range of stand conditions. An important finding was that WLA was a better predictor of survival probability than measured basal area increment. Effects of site index, age, and planting density on light capture growth relationships are also discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".