Estimating damage from selective logging and implications for tropical forest management
Bibliographic record
Abstract
The proportion of stems damaged by logging is a key parameter for the management of natural productive forests in the three tropical continents (Africa, Neotropics, and southeastern Asia). Based on a review of the literature and on a meta-analysis of published data, we estimated this logging damage rate for conventional logging and compared it across continents. Scaling coefficients were estimated to convert damage rate and logging intensity from one unit to another. Felled trees were smaller in the Neotropics (61 cm diameter at breast height (dbh) on average) than in Africa or Asia (92 cm dbh). A pantropical equation relating the proportion of trees damaged (α, no unit) to logging intensity (N log , in ha –1 ) was fitted: α = 1 – (1 + 0.09135N log ) –0.70461 . A significant residual continent effect was found, with lower damage in the Neotropics than in Africa or Asia for the same level of logging intensity, in agreement with the size of felled trees. The damage rate varied with the size of damaged trees and divided equally between destroyed and injured trees, with injured trees experiencing a threefold mortality rate during 5–10 years. Taking account at least of the relationship between logging damage and logging intensity would improve the accuracy of forecasts in forest management.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".