Above-ground Biomass Recovery in Managed Tropical Forest in the Jari Valley, Eastern Amazon
Bibliographic record
Abstract
Tropical forests play an important role in maintaining the regional rainfall regime and global climate, besides representing a significant stock of carbon. This study aimed at evaluate above-ground biomass (AGB) recovery, after reduced-impact logging (RIL) in a managed forest on the Jari River valley. The data were collected in 15 plots (100 m × 100 m) in the management area of the Jari Florestal Company. To estimate AGB we used a local equation adjusted for forests in the eastern Amazon. AGB before logging ranged from 157.9 Mg ha-1 to 619.9 Mg ha-1, with an average of 362.5 Mg ha-1. AGB after logging ranged from 151.2 Mg ha-1 to 632.8 Mg ha-1, with an average of 322.4 Mg ha-1. The time of monitoring of the plots and logging intensity were the main factors that influenced the recovery of the AGB. In 12 years after the RIL, the forest was able to recover its initial stocks of AGB, in places of low exploitation intensity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".