Crown Efficiency and Pine Cones Production for Brazilian Pine (Araucaria angustifolia (Bertol.) Kuntze) in South Brazil
Bibliographic record
Abstract
Crown efficiency is a relation of growth with the area tree occupies (available area efficiency). Efficiency is associated with the vertical and lateral expansion capacity of the tree and crown, increase, density and light absorption. Thus, our study aimed to evaluate crown efficiency with pine cones∙tree-1 production and dendro/morphometric variables of individual araucaria trees in three forest sites in south Brazil. In each site of each tree, dendrometric and morphometric variables were measured and increment rolls were taken at the breast height diameter level. Together with these data, the morphometric indexes, annual periodic increment in diameter of the last ten years (APId) and efficiency of the crown as a function of APId by the horizontal projection area of the crown (hpac) and number of pine cones∙tree-1 (np) by hpac were calculated. Crown efficiency was modeled as a function of the independent variables hpac, APId, crown length (cl), crown ratio (cr) and np. The accuracy of the adjustment was evaluated by the statistics of the deviance, Akaike’s information criterion and graph of the residuals. The results show that the number of pine cones∙tree-1 increases with annual periodic increment in diameter and diameter. Crown efficiency decreases with increasing hpac, cl and cr, indicating silvicultural treatments should be performed with the removal of trees of larger diameter and crown size. A higher pine cones production and efficiency correlates with forest growth, shape, competition, sociological position (stratum tree occupies) and density.
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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 | 0.001 | 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".