Thinning Increases Individual Tree Growth While Reducing the Growth Heterogeneity of Lodgepole Pine
Bibliographic record
Abstract
The positive effect of thinning on individual tree growth is well known, but the subsequent growth dynamics of individual trees over a rotation is relatively unknown, even though this is critical for interpreting actual thinning effects. Therefore, in this study, we aimed to investigate the thinning response of individual tree growth dynamics of lodgepole pine (Pinus contorta var. latifolia) over rotation using an individual tree growth model. We used data from an operational site to use as input to the Mixedwood Growth Model, an individual tree growth model, to simulate tree growth throughout the rotation. Overall, we found that thinning increased the absolute growth of individual trees and reduced the growth heterogeneity throughout the rotation. Combining pre-commercial thinning prior to commercial thinning provided maximum growth and less growth variability in individual trees. The positive effect of thinning was immediate and declined with time since thinning with most of the responses occurred within the first 10–15 years of thinning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".