Pre-Commercial Thinning in Boreal Mixedwood: Change in Site Resource Availability and Modelled Growth
Bibliographic record
Abstract
Pre-commercial thinning is a silvicultural treatment that can increase stand merchantable timber production and resistance to drought. Despite these benefits, its use in Alberta remains limited. Processes driving growth response to density management are poorly understood but important when applying thinning to stands that will grow under future warmer and drier conditions. Consequently, we evaluated microclimate and resource availability in operational scale pre-commercial thinning trials of young (19 year old) boreal trembling aspen/white spruce/paper birch mixedwoods in northern Alberta, Canada. Thinned stands in this study experienced more frequent temperature extremes, higher soil moisture, and greater heat sum when compared to unthinned stands. Soil nutrient supply rates were not different between treatments, nor was soil moisture during wet periods, soil temperature in the early and late parts of the growing season, or quantity of extreme low soil moisture values. Regeneration of broadleaf trees species in thinned stands was substantial. Pre-commercial thinning created an improved but also more extreme tree growing environment, highlighting a need to evaluate the treatment from a risk perspective. Available tools to evaluate growth response caused by pre-commercial thinning include two growth models, MGM and GYPSY. We evaluated the consistency of projected thinning effects within and between estimates by both models to gauge their reliability. GYPSY’s projected thinning effect was greater than MGM’s projected effect, and significant differences between models were found in both the magnitude and direction of projected thinning effects. Still, neither model showed a significant average treatment effect. While these results cannot be used to determine model accuracy, their variability highlights limitations in interpreting any single model projection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".