Factors influencing the achievement of steady state in mercury contamination among lakes and catchments of south-central Ontario
Bibliographic record
Abstract
Mercury (Hg) concentrations in recent (0.5–1 cm) and preindustrial (>30 cm) sediments were examined across lakes in south-central and eastern Ontario, Canada (45.53°N, 82.41°W to 44.15°N, 76.25°W), to determine whether Hg exported from watersheds is at steady state with atmospheric deposition. An examination of headwater lakes revealed that Hg enrichment was not uniform among watersheds but that the enrichment factor (EF = [Hg]present day/[Hg]preindustrial, standardized for organic matter) decreased as a function of drainage ratio (Ad /Ao, watershedarea/lakearea). Furthermore, the model fit was improved after accounting for differences in sulfate concentrations and pH among lakes: EF = (Ad /Ao)–15.96 – 0.07(SO 42– ) – (3.55(pH>8.3)) (R2 = 0.458, p = 0.0001). Hg concentrations in preindustrial sediments of headwater lakes showed a positive linear relationship with drainage ratio (partial t = 4.83, p < 0.0001, n = 66) that was strengthened following an adjustment for mean annual runoff (MAR) ([Hg]preindustrial = 0.011 ± 0.002(Ad /Ao) + 0.0008 ± 0.0003(MAR) (R2 = 0.108, F[1,66] = 8.01, p = 0.006)). Our results suggest that Hg export from watersheds may be currently lagging behind atmospheric Hg deposition, in which case, Hg export would increase into the future, even as Hg deposition from the atmosphere stabilizes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".