Modelling Climate Effects on Site Productivity and Developing Site Index Conversion Equations for Jack Pine and Trembling Aspen Mixed Stands
Why this work is in the frame
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Bibliographic record
Abstract
Forest site productivity estimates are crucial for making informed forest resource management decisions. These estimates are valuable both for the tree species currently growing in the stands and for those being considered for future stands. Current models are generally designed for pure stands and do not account for the influence of climate on tree growth. Consequently, site index (SI) conversion equations were developed specifically for jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) trees grown in naturally originated mixed stands. This work involved sampling 186 trees (93 of each species) from 31 even-aged mixed stands (3 trees per species per site) across Ontario, Canada. Stem analysis data from these trees were utilized to develop stand height growth models by incorporating climate variables for each species. The models were developed using a mixed effects modelling approach. The SI of one species was correlated with that of the other species and climate variables to establish SI conversion equations. The effect of climate on site productivity was evaluated by projecting stand heights at four geographic locations (east, center, west, and far west) in Ontario from 2022 to 2100 using the derived stand height growth models. Height projections were made under three emissions scenarios reflecting varying levels of radiative forcing by the end of the century (2.6, 4.5, and 8.5 watts m−2). Climate effects were observed to vary across different regions, with the least and most pronounced effects noted in the central and far western areas, respectively, for jack pine, while effects were relatively similar across all locations for trembling aspen. Stand heights and SIs of jack pine and trembling aspen trees grown in naturally originated mixed stands can be estimated using the height growth models developed here. Similarly, SI conversion equations enable the estimation of the SI for one species based on the SI of another species and environmental variables.
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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 | 0.000 |
| 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 it