The Use of Volume Yield and the Number of Trees to Control Forest Management Operations and Combat Illegal Harvesting
Bibliographic record
Abstract
There is a lack of understanding on the relationship between the authorized volume of wood harvest in Sustainable Forest Management Plans (SFMP) in the Amazon, the volumes listed in the official systems of volume control, the actual harvested volumes, and the consequences these parameters have for the illegal wood market.The objective of this study was to evaluate the production and volume yield and the number of harvested trees as part of forest management plans in public and private forests through analysis of harvest data from 85 SFMP registered in the official electronic system of control of forest products in the state of Pará. The forest management plans were categorized into public (federal and state), and private, with these being further subdivided into having one or more than one annual production unit, and community-managed forest. This analysis was based on the hypothesis that production and volume yield from SFMP in public and private forests did not differ. Calculations were made to test this hypothesis, and these included forest harvest yield, the percentage of the number of trees harvested in the SFMP using the relationship between the authorized and harvested volumes as well as the numbers of authorized trees and those harvested.The results show that the yields based on numbers of trees and volumes were statistically lower for SFMP in public forests compared to private forests. These results suggest that a significant part of SFMP in private forests could systematically become a source of forest credits used to obtain fraudulent documents for forest products that are illegally harvested, which is referred to as “esquentamento” in Portuguese.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".