Seizing resilience windows to foster passive recovery in the forest-water interface in Amazonian lands
Bibliographic record
Abstract
of degraded land by 2030. Although regenerating forests recover biomass, biodiversity, and processes with time, the recovery trajectories may be uncertain due to past disturbances. Currently, there is a lack of knowledge to sustain the effectiveness of passive regeneration for the recovery of riparian forests and the adjacent waterbodies in the tropics, which may compromise the outcomes of ongoing and future tropical riparian restoration programs. We evaluated the drivers of riparian forest structural recovery and how this relates to stream conditions in 12 abandoned pasturelands in eastern Brazilian Amazonia. These pasturelands range across regeneration age (pasture (PA) - 0 to 4 years; young regeneration (YR) - 8 to 12 years; old regeneration (OR) - 18 to 22 years) and years of past land-use (PA - 23.25 average years of past land-use, YR - 18.25, OR - 7). We compared the conditions of these sites to 4 reference sites with conserved forests (REF, >100 years), where there was no recorded pasture use in the past. Short-term responses of forests and streams to passive regeneration indicated high ecosystem resilience after low to intermediate past land-use intensity, reflected in the improvement of stream ecosystems. Such high resilience is possibly attributable to low- to intermediate-intensity pasture-related disturbances, remaining forest matrix, and residual structures (e.g. roots, sprouts, and in-stream wood) observed in the area. Our results suggest a recovery by 12 to 20 years for riparian forests of this region. However, areas degraded by intensive land-use apparently showed delayed recovery. We conclude that seizing resilience windows (defined here as the period when ecosystems retain high potential resilience) is essential to foster passive recovery of riparian forests and streams more cost-effectively in the tropics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".