Bibliographic record
Abstract
Abstract. The escalating global demand for gold has fuelled the rapid expansion of artisanal and small-scale gold mining (ASGM), which has become the largest source of mercury (Hg) emissions worldwide. Here we synthesize current research on the pervasive contamination of agricultural systems by ASGM-derived Hg, identifying the key environmental pathways and subsequent risks to food security. Within these systems, Hg undergoes complex biogeochemical transformations, with the methylation of inorganic Hg into its highly neurotoxic form, methylmercury (MeHg), being a critical process. This is particularly pronounced in rice paddy systems, where microbial activity and favourable redox conditions facilitate Hg methylation, resulting in the bioaccumulation of MeHg in rice grains—a staple food for billions. However, this synthesis reveals that atmospheric uptake is important to total Hg loadings in rice, and more so in tissues of crops grown in unsaturated soils. Indeed, we stress the importance of assessing all potential uptake pathways of Hg in agricultural systems: foliar assimilation from air, uptake from soils/water (particularly MeHg in rice), direct deposition to surfaces, and consumption of contaminated crop tissues (by both humans and livestock/poultry), to delineate the source and ratios of the different pools of Hg within crops and their consumers. A common shortcoming in past studies of ASGM-derived Hg in agricultural systems is that they have commonly overlooked one or more of these uptake pathways. These findings underscore a significant threat to global food chains and human health through the consumption of Hg contaminated produce. Mitigating these risks requires an improved understanding of the quantity of emissions/releases from ASGM, input pathways, and Hg biogeochemical cycling and fate in agricultural landscapes, paving the way for targeted interventions and sustainable management strategies to protect vulnerable communities. We suggest that these goals can be achieved through strategic international and interdisciplinary collaborations, novel and accessible technologies, and care for the dissemination of scientific information to impacted communities.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.155 | 0.120 |
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".