Effects of overtopping on growth of white spruce in Alaska
Bibliographic record
Abstract
Early establishment of competing vegetation often presents an obstacle to the success of planted white spruce (Picea glauca (Moench) Voss) seedlings. We followed growth and development of white spruce and associated vegetation for up to 17 years in Alaska’s boreal forests to quantify roles of overtopping plant cover in suppressing conifers. The three study areas represented a range of site conditions of varying productivity and species of competing cover, different site preparation and release treatments, and different bare-root and container white spruce stock types. Herbaceous overtopping peaked early after planting and decreased as white spruce were able to outgrow competitors. Overtopping by shrubs and hardwoods, especially aspen (Populus tremuloides Michx.) and resin birch (Betula neoalaskana Sarg.) peaked somewhat later than herbaceous overtopping and decreased over time for most sites and treatments. In a model that combined all sites, vegetation management treatments, and years, overtopping and previous year’s volume explained approximately 85% of the variation in volume growth. Increasing the size of planting stock helped reduce overtopping, hence suppression, even in treatments dominated by hardwood species. Results suggested that control of overtopping was essential for maximum growth and long-term or increasing levels of overtopping severely suppressed white spruce seedling growth.
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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 | 0.000 |
| 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) | 0.001 | 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".