Heavy crown thinning in redwood/Douglas-fir gave superior forest restoration outcomes after 10 years
Bibliographic record
Abstract
Forest restoration thinning has the potential to enhance the structural complexity and accelerate the development of large trees important to wildlife, aesthetics, and wildfire resistance. These are key objectives for the restoration of even-aged secondary forests within Redwood National Park in Humboldt County, CA, USA. We evaluated the tree growth and stand structure 10 years after two thinning methods were applied at two intensities in a 40-year-old mixed redwood ( Sequoia sempervirens (Lamb. ex D. Don) Endl.)/Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii) stand. Heavy thinning enhanced the diameter growth of redwood and Douglas-fir trees more than light thinning. Crown thinning generally enhanced the structural diversity more than low thinning, and structural diversity increased progressively over the 10 years following thinning. Understory plant richness fluctuated between measurement years. Heavy thinning enhanced the understory shrub cover. The fastest-growing trees in heavily thinned stands were much more likely to sustain bear damage, especially redwood trees. Overall, different thinning methods and intensities induced a different suite of outcomes, yet none restored redwood dominance, but all treatments enhanced some other ecosystem values important for old-growth restoration such as large overstory trees, understory plant and shrubs, and elements of structural complexity, including tree-size variability, snags, down logs, and trees exhibiting stem or top damage.
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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 | 0.001 | 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".