Ficus insipida tree rings as biomonitors for gaseous elemental mercury in the artisanal gold mining-impacted Peruvian Amazon
Bibliographic record
Abstract
Introduction The primary source of anthropogenic atmospheric mercury (Hg) emissions globally is artisanal and small-scale gold mining (ASGM). Estimates of Hg emissions from ASGM are poorly constrained due to a lack of monitoring data and the informal, generally unregulated nature of this industry. Trees accumulate atmospheric gaseous elemental mercury (GEM) in bolewood following stomatal uptake and thus have the potential to be used as biomonitors to quantify the spatial and temporal footprint of Hg emissions from ASGM. Methods We collected tree cores from Ficus insipida at three mining-impacted and two remote, unimpacted sites in the Peruvian Amazon (n = 4 trees per site). Results We show that tree ring Hg concentrations were higher near ASGM activity located near mining towns (6.0 ng g −1 ) compared to remote sites (0.9 ng g −1 ) and recent tree rings were strongly linearly correlated with atmospheric GEM concentrations across all sites (p < 0.0001, r 2 = 0.64), especially in the dry season when there is enhanced ASGM activity (p < 0.0001, r 2 = 0.76), highlighting the potential for tree rings to be used as biomonitors for GEM. At the most impacted sites, tree-ring Hg increased over time in response to intensification of ASGM. Discussion Thus far, applications of dendrochemistry to quantify Hg pollution have been largely restricted to coniferous species in temperate regions, but this study shows that tropical species also quantify Hg pollution. We conclude that Ficus insipida is a suitable biomonitor and powerful tool for characterizing the spatial, and potentially temporal footprint of GEM emissions from ASGM in the neotropics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".