Longevity and dynamics of fatally and nonfatally topped Douglas-fir in the Coast Range of Oregon
Bibliographic record
Abstract
Worldwide, snags are an important, but often lacking, component of forest ecosystems. We revisited artificially topped Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) trees 16–18 years after treatment in a replicated experiment in western Oregon. Some trees had been topped such that no live crown was retained (fatally topped), while others retained some portion of their live crown after topping (nonfatally topped). Topped trees were created under three different silvicultural regimes: clearcut, two story, and group selection. Twenty-three percent (61 of 262) of nonfatally topped trees remained living 16–18 years after treatment; 4% (19 of 482) of fatally topped trees had broken at some point up the bole by 16–18 years after treatment. Silvicultural regime, post-treatment height, stem diameter, stem lean, and ground slope were considered as potential explanatory variables in logistic regression models explaining mortality and breakage. A nonfatally topped tree’s odds of surviving 16–18 years after treatment was greater in the mature matrix of group selection stands than in clearcuts or two-story stands. A fatally topped tree’s odds of breaking within 16–18 years of treatment decreased as DBH increased. If carefully created, artificially topping trees can be a useful silvicultural tool to increase structural heterogeneity.
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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".