Creating boreal mixedwoods by planting spruce under aspen: successful establishment in uncertain future climates
Bibliographic record
Abstract
Planting white spruce (Picea glauca (Moench) Voss) under established aspen (Populus tremuloides Michx.) stands has substantial potential for regenerating mixedwood ecosystems in the western Canadian boreal forest. The presence of an aspen overstory serves to ameliorate frost and winter injury problems and suppresses understory vegetation that may compete with white spruce. Under future climatic regimes with more frequent and severe drought episodes, underplanting may be a cost-effective strategy for lowering the risk of mortality in mixedwood regeneration. We examine the growth of white spruce during the first 18 years after being planted beneath a 39-year-old stand of trembling aspen. Treatments included thinning from over 6000 stems·ha−1 to 3000, 2000, and 1000 stems·ha−1 and fertilization. Initial stimulation of understory vegetation by fertilization had no measureable effect on spruce heights or diameters at year 18. Aspen thinning treatments did not have a significant effect on spruce height growth rates after spruce crowns had emerged above the understory shrub layer due to rapid aspen basal area increases after thinning. Small, but significant, increases for spruce height and diameter were present in the 1000 and 2000 stem·ha−1 aspen thinnings. A much wider range of aspen stand conditions may be suitable for planting spruce to create mixedwood ecosystems than has been previously considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".