Contrasting the temporal dynamics of stand structure in even- and uneven-sized <i>Picea abies</i> dominated stands
Bibliographic record
Abstract
Although proposed as a means of increasing structural diversity in managed forests, the impact of single-tree selection on the temporal dynamics of three-dimensional structure has not been previously evaluated. Forest structural development in Picea-dominated stands was contrasted over 15 years in stem-mapped randomized plots in southern Finland that underwent either low thinning (creating the even-sized (ES) structure of a bell-shaped diameter distribution) or single-tree selection (maintaining the uneven-sized (UES) structure of a reverse-J-shaped distribution) through multiple harvest entries. Structure was quantified with nonspatial stand attributes (e.g., density) and indices that quantify spatially explicit relationships among neighboring trees (e.g., structural complexity index (SCI)). Over time, three-dimensional structure reflected differential tree growth and mortality, resulting in minor changes in tree composition, spatial pattern, and tree size differentiation and somewhat greater changes in the SCI. The third harvest entry simplified the forest structure in both structure types. However, structural metrics such as the variability of tree diameters, tree size differentiation, and the SCI recovered to preharvest levels within 2–4 years in UES plots, whereas no recovery was seen in the ES structure type. Single-tree selection was demonstrated to perpetuate the uneven-sized structure associated with natural nonpyrogenic Picea-dominated forests.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".