Spatial patterns of tree regeneration and ground cover in dry Scots pine forest in Russian Karelia
Bibliographic record
Abstract
ABSTRACT The spatial patterns of Scots pine recruitment and ground layer vegetation are heterogeneous in dry pine forests. The goal of our 1994–2004 work in the dry pine forests of Russian Karelia was to find the reasons for this heterogeneity at the meso-and fine levels of scale. Twenty-seven sample plots (30 × 30 m) with different tree age structures, stand densities, and times since fire were examined. In each sample plot the tree layer, saplings, and ground layer were described in detail using a coordinate grid. Needle, bark, and branch debris was also measured. Detrended canonical correspondence analysis (DCCA) and analysis of variance (ANOVA) confirmed the conclusion of earlier studies that time since fire and stand structure determine the formation of recruitment and ground layers at the patch level. Ripley's K-function analysis showed that the spatial pattern of recruitment was clustered in both even-aged and uneven-aged forests. The bivariate analysis showed a repulsion effect between mature trees and recruitment in even-aged forests, and attraction between mature trees and saplings in uneven-aged forests. Analyses of tree layer, recruitment, and ground layer spatial patterns in the sample plots proved that the spatial pattern of regeneration and its intensity at the fine scale were related to the ground layer structure. At this scale, ground layer community structure was generally determined by local canopy projection and the amount of needle, branch, and bark debris.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".