Climatic and anthropogenic effects on atmospheric mercury\naccumulation rates in ombrotrophic bogs from Southern Ontario
Bibliographic record
Abstract
\nTo quantify the effects of human activities on atmospheric deposition of mercury in eastem Canada, an\nimproved understanding of the natural variations of the concentrations, fluxes and sources of Hg over a long period\nof time is required. Peat cores from 3 sites in southern Ontario were used to reconstruct changes in atmospheric\nmercury accumulation rates for the past 10,000 years. The net mercury accumulation rates and excess mercury\n(mainly anthropogenic) were calculated using the long-terrn average Hg/Br and Hg/Se. The average background\nmercury accumulation rate during the pre-anthropogenic period was $1.4 \\pm 1.0~\\mu$g m$^{-2}$ yr$^{-1}$. An excess of Hg was\nobserved only once during that period, probably reflecting a change in climat. Mercury contamination from\nanthropogenic sources began about AD 1475 at the Luther Bog, corresponding to extensive biomass burning for\nagricultural activities by Native North Americans. During the late 17$^{\\rm th}$ and 18$^{\\rm th}$ centuries, deposition of\nanthropogenic Hg was at least equal to that of Hg from natural sources. Hg pollution increased again at the\nbeginning of the 19$^{\\rm th}$ century. The maximum increase (up to 30 times) compared to “background” occurred during\nthe 1950s, when the anthropogenic component represented up to 85% ofthe total atmospheric mercury deposition.\n
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".