Changes in structural inequality in Norway spruce stands on peatland sites after water-level drawdown
Bibliographic record
Abstract
Size structural dynamics of naturally established Norway spruce (Picea abies (L.) Karst.) stands growing on peatlands drained for forestry were investigated. The study was based on modelling of diameter at breast height (DBH) distributions of repeatedly measured stands in southern Finland. The Weibull function was used to parameterize the DBH distributions and mixed linear models were constructed to characterize the impacts of different ecological factors on stand dynamics. Initially, the positive skewness of the DBH distributions increased after drainage as a result of increases in stem numbers and a reduction in mean diameters. Simultaneously, the size inequality among trees increased. These changes were due to regeneration and (or) ingrowth and indicated only little competition from the larger trees. Subsequently, the DBH distributions changed from positively skewed to normal and finally to negatively skewed resulting from tree growth and a reduction in the number of small DBH trees. This indicated increased asymmetric intertree competition. Size inequality did not change during this later stage in stand development, suggesting a concurrent component of symmetric competition. Thinnings had little impact on DBH distribution trends. The observed stand dynamics allow the allocation of growth resources to the desired crop component by appropriate silvicultural treatments.
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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".