Spatial Dependence of the Site Index of Pinus taeda L. Production Forests, in the Southern Central Region of the State of Paraná, Brazil
Bibliographic record
Abstract
A spatial analysis of the site index used for the classification of Pinus taeda production forests was performed using dominant height data from 402 continuous inventory plots. The data were examined with simple descriptive statistics and fit with four semivariogram models by the GS + program. The best model was then used to predict the site index in unsampled areas by ordinary kriging in ArcView. All models showed that site index values exhibited spatial dependence, with the degree of spatial dependence ranging from strong to moderate. The spherical model was used for kriging. In this model, the degree of spatial dependence was 29% and the range was 5,330 m, with a residual sum of squares (RSS) of 3.00 and coefficient of determination (r²) of 0.776. Measured and predicted values were compared by cross-validation, which produced a linear regression of observed versus predicted value with a slope coefficient of 1.068, slope standard error of 0.070, and intercept coefficient of -1.45. The site classification map generated by kriging divided the studied forests into five classes. Before kriging, all of the forest stands had one global average value for the site index, but after kriging this was changed to there being two or three values of the site index for many stands. Ordinary kriging proved to be an optimal method for interpolating the site index of unsampled areas to permit their classification, as is the case for young plantations for which inventory samples have not yet been taken.
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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 | 0.001 |
| 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) | 0.000 | 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 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".