Competition Indices and Their Relationship With Basal Area Increment of Araucaria
Bibliographic record
Abstract
Models that report the effect of competition are important for forest management since forests with higher levels of competition have lower increment rates, and their use is necessary to plan forest interventions. Thus, this study aimed to assess the effect of competition in the basal area increment of individual trees of Araucaria angustifolia (Bertol.) Kuntze in a natural forest. A total of 397 subject trees were measured, covering the diametric range. The dendrometric and morphometric characteristics of subject trees and their competitors were obtained, and 22 distance-dependent and distance-independent competition indices were calculated, in addition to increment cores extracted radially from the trunk at diameter at breast height. The relationship between models of periodic annual increment in basal area based on competition indices has allowed to obtain R2 values of 0.425 and Syx% ≥ 50.2. The multivariate technique of principal component analysis has shown that three principal components explain 78.43% of total variation. The first component was responsible for explaining 52.95%, with similar eigenvector for 11 competition indices, evidencing that these models can be used to describe especies competition, although they show different variables and mathematical equations in calculations. Results show the importance of competition to predict increment of Araucaria in individual trees.
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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 | 0.000 | 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".