Limited biomass recovery from gold mining in Amazonian forests
Bibliographic record
Abstract
Abstract Gold mining has rapidly increased across the Amazon Basin in recent years, especially in the Guiana shield, where it is responsible for >90% of total deforestation. However, the ability of forests to recover from gold mining activities remains largely unquantified. Forest inventory plots were installed on recently abandoned mines in two major mining regions in Guyana, and re‐censused 18 months later, to provide the first ground‐based quantification of gold mining impacts on Amazon forest biomass recovery. We found that woody biomass recovery rates on abandoned mining pits and tailing ponds are among the lowest ever recorded for tropical forests, with close to no woody biomass recovery after 3–4 years. On the overburden sites (i.e. areas not mined but where excavated soil is deposited), however, above‐ground biomass recovery rates (0.4–5.4 Mg ha −1 year −1 ) were within the range of those recorded in other secondary forests across the Neotropics following abandonment of pastures and agricultural lands. Our results suggest that forest recovery is more strongly limited by severe mining‐induced depletion of soil nutrients, especially nitrogen, than by mercury contamination, due to slowing of growth in nutrient‐stripped soils. We estimate that the slow recovery rates in mining pits and ponds currently reduce carbon sequestration across Amazonian secondary forests by ~21,000 t C/year, compared to the carbon that would have accumulated following more traditional land uses such as agriculture or pasture. Synthesis and applications . To achieve large‐scale restoration targets, Guyana and other Amazonian countries will be challenged to remediate previously mined lands. The recovery process is highly dependent on nitrogen availability rather than mercury contamination, affecting woody biomass regrowth. The significant recovery in overburden zones indicates that one potential active remediation strategy to promote biomass recovery may be to backfill mining pits and ponds with excavated soil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".