The Influence of Stocking and Stand Composition on Productivity of Boreal Trembling Aspen-White Spruce Stands
Bibliographic record
Abstract
We analyzed productivity, in terms of periodic annual increment (PAI) in volume, of pure and mixed unmanaged naturally regenerated boreal stands mainly comprised of Populus tremuloides Michx. (trembling aspen) and Picea glauca (Moench) Voss (white spruce) ranging from 25–260 years old, in Alberta, Canada. Measures of density, site occupancy (Reineke’s stand density index-SDI), height, and site quality were evaluated in non-linear regression models aiming to explain the variation in PAI for the separate component species and for the entire stand. Analyses also included examination of the influence of stand composition. Results indicated a positive effect of both density of deciduous trees and of the percentage of deciduous tree stand basal area on trembling aspen volume PAI. White spruce PAI was positively influenced by spruce and aspen basal area, although effects of aspen were small. Height of both aspen and spruce were the most consistent independent variables in the models tested. Maximum PAI was achieved in mixed stands when site occupancy of both species was at its maximum, indicating the ecological combining ability of these two species. Variation in maximum stocking densities (i.e., stockability) has a strong influence on increment in these forests and is a major factor leading to increased productivity of mixed compared to pure stands.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".