SURVIVAL AND GROWTH OF UNDER-PLANTED TREES: A META-ANALYSIS ACROSS FOUR BIOMES
Bibliographic record
Abstract
The transformation of natural forest regeneration processes by human activities has created the need to develop and implement new models of forest management. Alternative silvicultural systems such as variable retention harvest, partial and patch cuts, and older forest management practices such as under-planting, are used in many forests around the world, particularly in North American oak stands, the boreal and coastal temperate rain forests of Canada and the United States, and in many degraded tropical regions of Asia and the Americas. Specific objectives are pursued in each of these biomes, but some are common to most regions, such as preservation of cover and structure and their associated benefits for natural or artificial regeneration due to moderation of the microclimate, development of optimal light and competition conditions, and reduced predation by herbivores. Shelterwoods are often presented as an alternative to clear-cutting to improve the survival of planted trees. A meta-analysis of published results with randomization tests was performed to test the relationship between overstory density and planted seedling growth and survival. Multiple comparisons were also used to reveal optimal levels of overstory density, if they exist. A majority of studies show that survival and growth improve as stand density decreases to an intermediate level, below which they either drop or stabilize. This level seems optimal in most conditions, as it is also more apt to fulfill other objectives imposed on today's forest activities, such as the conservation of forest processes and structures, and the reconstruction of degraded stands through the accelerated return of mid- to late-successional species.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.048 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".