Wood density and leaf traits independently relate to growth rate of naturally regenerated tree species in <i>Araucaria angustifolia</i> plantations in the Atlantic Forest, Argentina
Bibliographic record
Abstract
Tree plantations can facilitate the establishment of native trees, thus providing opportunities for the ecological restoration or rehabilitation of degraded forest lands. Stand variables can influence the establishment of native tree species, but few studies have been carried out to determine how sapling growth response to stand variables varies among functionally different species. We evaluated the effect of stand age, stand basal area, tree density, and time since last logging on stem diameter growth rates, as well as the dependence of such effect on whole plant, stem, and leaf functional traits. We measured the stem diameter increment on 280 individuals of 22 species in permanent plots for a period of 2 years in araucaria ( Araucaria angustifolia (Bertol.) Kuntze) plantations in the Atlantic Forest, NE Argentina. Increasing all-species basal area and plantation age negatively affected the growth rates, and such effect tended to be higher in species with low wood density. Two functional axes independently explained growth rate differences between species in growth rate. Growth rates increase with decreasing wood density, increasing leaf phosphorus and potassium content, and decreasing specific leaf area. Among species that are similar in the traits associated with the first functional axis, plant growth increases with increasing leaf nitrogen content.
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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 | 0.000 | 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".