The influence of competition and species mixture on plantation-grown white spruce: Growth and foliar nutrient response after 20 years
Bibliographic record
Abstract
A 20-year-old experimental white spruce plantation was used to identify key stand (neighbourhood competition) and soil (physical and chemical properties) factors influencing spruce growth (Periodic Basal Area Increment) and foliar nutrients. Total and species-specific competition was estimated using Hegyi’s distance-dependent index for 39 individual spruce trees. Twelve trees, covering the range of total HCI (2 to 8) and aspen competition (0% to >75%), were selected for repeated (May through October) foliar sampling. Spruce PBAI declined approximately 10% for each additional unit of total HCI; species did not significantly affect this decline. Increasing aspen presence significantly influenced spruce foliar N (1.17% to 1.31%), P (0.15% to 0.23%), and K (0.68% to 0.88%), but led to declines in Ca (0.81% to 0.48%). Multiple linear regression indicated that soil carbon (partial r 2 = 0.386) and available soil moisture (partial r 2 = 0.131) together explained more of the variation in spruce growth than did competition factors alone (partial r 2 = 0.251). The results suggest that, at this stage of stand development, precommercial thinning operations should focus on density control and inter-tree spacing, while retaining an aspen component resulting in well-spaced, free-growing mixed stands of white spruce and aspen.
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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 | 0.001 |
| 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".