The Efficiency of Sampling Methods for Dendrometric Estimates of Thinned Stands of Pinus taeda L. in Santa Catarina, Brazil
Bibliographic record
Abstract
This study aimed to compare the efficiency of the sampling methods: Fixed Area, Bitterlich, Prodan and Modified Prodan to estimate the commercial volume and other dendrometric estimators for a 34 years old of Pinus taeda L. stands located in Campo Belo do Sul, Santa Catarina, Brazil. It were distributed a total of 10 sample units of the following methods: Fixed Area with 200, 400 and 500 m² of area, Bitterlich, Prodan and Modified Prodan were distributed, both with 6, 7, 8, 9 and 10 trees. In addition to collecting dendrometric data, the installation time of the sample units was timed, whereby the relative efficiency for each method was calculated. The comparison between the harvest volumes and the volumes estimated by the methods was performed by the Skott Knott test, and the results that did not differ statistically were weighted by the parameters of relative error, relative efficiency and proximity to harvest. All variations of the Modified Prodan and Prodan methods had sample insufficiency. The number of trees per hectare presented higher values for the 200 m² Fixed Area method and lower values for Prodan with 10 trees. Prodan with 6 trees got the shortest time. The Bitterlich method obtained sample adequancy at 10% error and presented the best result. Among the alternative methods to Fixed Area, Modified Prodan with 7 trees can be indicated for pilot inventory. However, when more precise results are needed, the Bitterlich method is indicated.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 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".