Influence of herbaceous and woody competition on white pine regeneration in a uniform shelterwood
Bibliographic record
Abstract
We investigated the effects of herbaceous and woody vegetation control on the survival and growth of planted and natural eastern white pine (Pinus strobus L.) seedlings through six growing seasons following a uniform shelterwood regeneration harvest on two independent sites. Subsequent to chain scarification, white pine seedlings were planted at 2-m spacing, augmenting natural regeneration (full stocking and >3000 seedlings per ha). Herbaceous vegetation control involved the suppression of grasses, forbs, ferns, and low shrubs, and was maintained for zero, two, or four growing seasons after planting. Woody control involved the removal of all tall shrubs and deciduous trees, and was conducted at the time of planting, at the end of the second or fifth growing seasons, or not at all. White pine seedling growth responded positively to increased duration of herbaceous vegetation control and negatively to delayed woody control. Maximum growth was not realized unless both types of vegetation were suppressed. During the first six growing seasons, the height growth of planted pine was more than twice that of naturally regenerating pine, regardless of tending regime. The study suggests that successful white pine regeneration may be achieved by thinning from below to allow 50% to 60% of full sunlight in the understory, followed by the proactive, early suppression of woody and herbaceous vegetation to maintain optimum light levels and reduce competition for soil moisture and nutrients.
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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".