Height and volume functions for<i>Pinus lawsonii</i>,<i>Pinus leiophylla</i>,<i>Pinus oocarpa</i>and<i>Pinus pringlei</i>plantations in Guarei, São Paulo, Brazil
Why this work is in the frame
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Bibliographic record
Abstract
Inventories are time- and money-consuming. Hence, accurate equations to estimate difficult-to-measure variables are desirable, especially for species that are not commercially established, such as Pinus lawsonii, P. leiophylla, P. pringlei and P. oocarpa. This study aimed to fit height and volume models for these species and to present stand attribute values for a plantation located in Guarei, Brazil, belonging to the company Resinas Brasil. Models were assessed by the adjusted coefficient of determination, mean square error and residual plots. For P. lawsonii and P. pringlei, the best hypsometric models were those of Van Soest and Mishailof, respectively. For P. leiophylla at 3 m × 1.5 m and 3 m × 3 m spacing, the best models were the linear and Mishailof models, respectively. For P. oocarpa planted at 3 m × 1.5 m and 3 m × 3 m spacing, the best models were those of Mishailof and Van Soest, respectively. The best volume model was the logarithmic Spurr for all species, except for P. oocarpa, where the Spurr model was the best. The mean stem form factor for all species was 0.53. Mean annual increment ranged from 8.4 to 24.5 m3 ha−1 (P. lawsonii and P. oocarpa), which can be considered satisfactory for plantations without genetic improvement and fertilisation, enforcing the species’ commercial potential.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it