Quantifying the vertical diversification development stage of old-growth Douglas-fir to derive stage-specific targets for restoration silviculture
Bibliographic record
Abstract
Structural variability in natural Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) forests hinders restoration targets for the full old-growth developmental sequence. To guide stage-specific restoration, we present a new approach toward developing a simple index of the vertical diversification (VD) stage. VD-associated sub-stand structures were objectively identified by quantifying the size structures of live trees across a developmental sequence of 10 pristine stands in the Oregon Cascades, USA. Floating neighborhoods were used to delineate natural tree neighborhoods based on triangulated irregular networks in five concentric rings of ever-greater spatial extent (averaging 60–2060 m2). Diameter distributions summarized the most frequently encountered tree size structures among neighborhoods, with increasing deviation from the neutral multi-variate core. Of 18 observed diameter distribution types (DDTs), the core DDT characterized all-sized tree neighborhoods reflective of vertical diversification, which was most abundant in early old-growth (VD) stands and least abundant at the extremes of the sequence. VD declined in older stands, whose more distinct DDTs had peaks in larger trees and multiple size classes, likely reflecting horizontal diversification (HD). This new approach illustrates that structural restoration of VD stands could be facilitated by the single-tree selection method, while that of HD stands may be promoted with both single-tree and group selection as well as targeted release.
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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 | 0.001 | 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) | 0.000 | 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".