Reliance on <sup>210</sup>Pb Chronology Can Compromise the Inference of Preindustrial Hg Flux to Lake Sediments
Bibliographic record
Abstract
Lake sediments are frequently used to reconstruct the rate and magnitude of human impacts on the biogeochemical cycle of mercury (Hg). The vast majority of these studies rely on excess (210)Pb inventories in short cores to temporally constrain recent trends in Hg deposition, revealing an approximately 3-fold increase in Hg deposition since preindustrial times. However, the exhaustion of unsupported (210)Pb and the onset of widespread global Hg pollution converge temporally in the late 19th century, raising the possibility that preindustrial Hg fluxes are poorly constrained. Here, we combine (210)Pb and accelerator mass spectrometry (AMS) (14)C dated lake sediment records from arctic and Andean lakes to assess the reliability of (210)Pb-derived chronologies in the estimation of preindustrial Hg fluxes. For all four studied lakes, relying on (210)Pb chronologies results in an overestimate of preindustrial Hg fluxes, because extrapolated basal (210)Pb sedimentation rates are systematically overestimated in comparison to accumulation models that include (14)C dates. In the Andes, the use of (14)C dates is critical toward assessing the full history of Hg pollution, which extends beyond the industrial era. In the Arctic, (14)C dating suggests that Hg deposition may have increased >10-fold since the Industrial Revolution, rather than the commonly quoted 3-fold increase. The incorporation of (14)C dates may therefore be necessary if accurate Hg flux histories are sought from oligotrophic lake sediments.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".