Implications of land-use history for forest regeneration in the Brazilian Amazon
Bibliographic record
Abstract
Understanding the dynamics of forest regeneration on abandoned agricultural land in the Amazon has often been restricted by limited knowledge of historical land use. This study compared time-series classifications of mature forest, nonforest, and regrowth generated from Landsat sensor data for areas north of the Brazilian cities of Manaus (1973–2003) and Santarém (1984–2003) to chronicle land histories and forest age. At Manaus, active land use prior to abandonment ranged from <1 year with no burning to >10–15 years with burning. Few forests were recleared on more than three occasions. From the mid-1980s, land was increasingly abandoned and, in 2003, over 75% of the deforested area supported regenerating forests, with several being older than 20 years. South of Santarém, forests were cleared up to seven times. In 2003, few regenerating forests were older than 10 years, and all land covers, but particularly mature forest, were damaged by extensive wildfires in 1993 and 1998. Based on previous research, the study concludes that the capacity of regenerating forests to recover biomass and tree species diversity will be reduced where prior land use is more intense, as in Santarém and some clearings north of Manaus.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".