Atmospheric Hg Emissions from Preindustrial Gold and Silver Extraction in the Americas: A Reevaluation from Lake-Sediment Archives
Bibliographic record
Abstract
Human activities over the last several centuries have transferred vast quantities of mercury (Hg) from deep geologic stores to actively cycling earth-surface reservoirs, increasing atmospheric Hg deposition worldwide. Understanding the magnitude and fate of these releases is critical to predicting how rates of atmospheric Hg deposition will respond to future emission reductions. The most recently compiled global inventories of integrated (all-time) anthropogenic Hg releases are dominated by atmospheric emissions from preindustrial gold/silver mining in the Americas. However, the geophysical evidence for such large early emissions is equivocal, because most reconstructions of past Hg-deposition have been based on lake-sediment records that cover only the industrial period (1850-present). Here we evaluate historical changes in atmospheric Hg deposition over the last millennium from a suite of lake-sediment cores collected from remote regions of the globe. Along with recent measurements of Hg in the deep ocean, these archives indicate that atmospheric Hg emissions from early mining were modest as compared to more recent industrial-era emissions. Although large quantities of Hg were used to extract New World gold and silver beginning in the 16th century, a reevaluation of historical metallurgical methods indicates that most of the Hg employed was not volatilized, but rather was immobilized in mining waste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.000 | 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 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".