Influence of Relative Density and Composition on Growth Rates in Boreal Mixedwoods
Bibliographic record
Abstract
Self-thinning or density-induced mortality is the consequence of competition between individuals of a stand and takes place when resources for tree development become insufficient for individuals needs or for the stand as a whole. Since density by itself is not a good indicator of competition between component species, other expressions -like Reineke’s stand density index (SDI) - have been proposed as suitable indicators of site occupancy and competition. Specific objectives of this research are: a) to develop density - size relationships and relative density indexes for aspen and white spruce mixtures and, b) to investigate the effect of composition and site quality on maximum density - size relationships and stand growth rates. We are using data from permanent sample plots for trembling aspen ñ white spruce stands from western Canada (Alberta, Saskatchewan and Manitoba) to examine the usefulness of density - size relationships in mixed and pure stands of these species. Modeling of these relationships is being done with data that cover a wide range of ages, densities, species compositions and site conditions. Multiple linear and non-linear regression are used to examine the relationship among maximum density and growth rates with quadratic mean diameter, composition, site quality and other site quality indicators. Our data suggest that a number of factors may influence the intercept value of the self-thinning lines but that the slope of these lines remains nearly constant. Preliminary results also show that the upper limit of density is related to quadratic mean diameter of the stand, composition (percentage of total basal area occupied by aspen) and site index. Our results will provide insight into the effects of composition and site quality on maximum density- size relations and stand growth rates, and into the potential application of these relationships in predicting mixedwood stand development.
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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 | 0.000 | 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 | 0.001 |
| 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 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".