Predicting natural regeneration of white spruce in boreal mixedwood understories
Bibliographic record
Abstract
Successful mixedwood management in the boreal forest of Alberta requires better knowledge of the occurrence and success of natural white spruce regeneration. In this study we developed statistical models to predict the natural establishment and height growth of understory white spruce (Picea glauca (Moench) Voss) in the boreal mixedwood forest in Alberta using data from 148 provincial permanent sample plots, supplemented by measurements of the amount and height growth of regenerating white spruce, and the amount and type of available substrate. A discriminant model correctly classified 73% of the sites as to presence or absence of a white spruce understory based on the amount of spruce basal area, rotten wood, ecological nutrient regime, soil clay fraction and elevation, although it explained only 30% of the variation in the data. On sites with a white spruce understory, a regression model related the abundance of regeneration to rotten wood cover, spruce basal area, pine basal area, soil clay fraction, and grass cover (R 2 = 0.36). About half of the seedlings surveyed grew on rotten wood, and only 3% on mineral soil, and seedlings were 10 times more likely to have established on these substrates than on litter. Exposed mineral soil was rare, covering only 0.3% of the observed transect area, rotten wood covered 4.5%, and litter/undisturbed forest floor covered the remainder. The regression models developed for average relative height growth rate included feather moss cover, stand age and birch basal area for seedlings ≤ 1 m (R 2 = 0.23), and feather moss cover, elevation, other moss cover and soil clay fraction for seedlings between 1 m and 3 m (R 2 = 0.27). Key words: Picea glauca, seedling establishment, seedbeds, site factors, coarse woody debris, predictive models, mixedwood management
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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 | 0.000 |
| 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 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".