Stockability, relative density and productivity: investigating their link in boreal mixedwoods
Bibliographic record
Abstract
I explore and evaluate the use of indicators of density and stand composition in analyzing key aspects of the dynamics of boreal stands comprised primarily by trembling aspen (Populus tremuloides Michx.) and white spruce (Picea glauca (Moench) Voss. in western Canada. First, using repeated measures data, I examine static and dynamic maximum size-density relationships (MSDR) for both pure and mixed stands of these species. Then, I evaluate the usefulness of density indicators in explaining understory light availability in mid-rotation and mature boreal pure and mixed stands, including examination of stand density index (SDI) based on the MSDR previously developed. Furthermore, I also test the usefulness of SDI and other density indicators in explaining trembling aspen, white spruce, and stand periodic annual increment in volume. Finally, I evaluate the usefulness of stand characteristics, including density and composition, in predicting the probability of survival of individual trees and saplings in boreal stands experiencing self-thinning. Results show that MSDR can be developed for mixed and pure boreal stands, and that a three-dimensional surface is the most suitable approach for their development. Stand composition and site quality are factors influencing MSDR. I also found that understory light is fairly variable in these stands, and that density and/or SDI are able to explain about 30 % of this variation. Total periodic annual increment in volume appears to be determined by the maximum stockability of these stands, and decreases in either aspen or spruce stocking, or both, result in reductions in PAI. Finally, one-sided competition, rather than two-sided, is the determining factor affecting individual tree survival, regardless of species. While basal area and/or SDI of larger trees captures these effects, individual tree growth rates serve better as indicators of survival probability.
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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 | 0.001 |
| 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 | 0.001 | 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".