Temporal variability of size–growth relationships in a Norway spruce forest: the influences of stand structure, logging, and climate
Bibliographic record
Abstract
In a forest stand, competition plays a central role, affecting individual growth. The size–growth relationship (SGR) indicates whether large trees grow proportionally more than (asymmetric SGR), equal to (symmetric), or less than (inversely asymmetric) smaller trees. SGR is thus an indicator of the growth partitioning and competition intensity within a stand. Using tree-ring analysis, we investigated long-term trends and interannual variability of SGR in several Norway spruce (Picea abies (L.) Karst.) stands in the Paneveggio Forest (eastern Italian Alps) over a 100-year period. The study plots were characterized by different stand structures (one multilayered and two monolayered) and disturbance histories (different dates of logging). Logging conducted until the 1940s induced an inversely asymmetric SGR in all the plots. During the successive five decades, in the monolayered plots, it shifted to direct asymmetric (plot 1) and to symmetric (plot 2). In the multilayered plot (plot 3), SGR remained inversely asymmetric. A direct effect of climate on SGR interannual variability was not found. However, fast-growing trees had a stronger climatic signal than slow-growing trees, indicating that growth rate affects tree response to climate. Moreover, we observed that sensitivity to climate was reduced in the monolayered plots over the study period, possibly as a consequence of increased competition.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".