Effects of thinning on tradeoffs between drought resistance, drought resilience, and wood production in mature Douglas-fir in western Oregon, USA
Bibliographic record
Abstract
Climate projections predict more frequent and severe drought in coastal Pseudotsuga menziesii forests of western North America, raising concerns over how to promote drought adaptation. Thinning often increases drought resistance (the ability to maintain growth during a drought) and resilience (the ability to recover growth after a drought), but these effects vary with thinning intensity, shift over time, and may have tradeoffs with fiber production. We collected tree cores from a long-term thinning study with four residual density levels replicated across both uniform thinning and thinning with gaps, and used annual growth data to investigate responses to droughts occurring 8 and 21 years after thinning. For the first drought, resistance and resilience were higher in treatments with lower residual densities. For the second drought, there were no differences in drought response between the lowest and highest residual density treatments, and all treatments had lower drought resistance and resilience than for the first drought. Spatial arrangement had little impact on drought resistance or resilience and residual density level had a significant effect on the periodic annual volume increment—drought resistance tradeoff. Our results suggest that thinning can promote drought adaptation in Pseudotsuga menziesii forests, but these effects dissipate over time.
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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 | 0.000 | 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".