Factors Influencing Directional Tree Felling in the Tapajós National Forest, Amazon, Brazil
Bibliographic record
Abstract
Given its complexity, directional felling is considered one of the most dangerous activities in the exploratory phase of forest management projects for timber obtention. Therefore, detailed studies of the variables influencing its execution are necessary. The present research was conducted in the Tapajós National Forest, Brazilian Amazon, and analyzed 1,075 trees logged using the directional felling technique in a 504.30 ha area. To better understand directional felling, the studied variables were subjected to descriptive analyses and principal component analysis, a multivariate procedure that enables the simultaneous evaluation of several variables. While the diameter, basal area, and stem and branch volume explained most of the variability concerning directional felling, the commercial height influenced the least. Trees of the species Hymenolobium petraeum (angelim pedra) strongly correlated with the dendrometric variables diameter and stem and branch volume. Those of the species Hymenaea courbaril (jatobá) showed a strong correlation with the commercial height. Pseudopiptadenia psilostachya (fava timborana), Dipteryx odorata (cumaru), Hymenaea parvifolia (jutai mirim), and Astronium lecointei (muiracatiara) had a strong correlation with the basic wood density. Trees of the species Couratari guianensis (tauari), Lecythis pisonis (sapucaia), Astronium lecointei (muiracatiara), Mezilaurus itauba (itaúba), and Goupia glabra (cupiúba) showed lower correlations with the time needed for planning, cutting, and felling. They also had a reduced correlation with the angular differences between the natural and effective and the intended and effective felling directions. The latter results suggest that these species do not follow a defined pattern concerning the directional felling technique. However, trees of the other species followed a different tendency. In general, the logged trees lacked correlation with the directional felling cutting and total operation time. The analyses suggest that as the diameter of a tree increases, the chances of completing its directional felling decrease.
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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) | 0.001 | 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".