Watershed characteristics and chemical properties govern methyl mercury concentrations within headwater streams of boreal forests in Ontario, Canada
Bibliographic record
Abstract
Methyl mercury (MeHg) concentrations in boreal headwater streams are influenced by complex natural processes and disturbances such as forestry management. Understanding drivers of MeHg within boreal streams in Ontario, Canada, is of particular interest as there are legacy MeHg concerns. However, models accounting for the complexity of underlying processes have not yet been developed. We assessed how catchment characteristics and stream water chemistry influence MeHg concentrations within 19 watersheds of the Dryden – Wabigoon Forest in Ontario, Canada, using a structural equation modelling (SEM) approach. Despite the study area encompassing a large variation of boreal forest watersheds in the Canadian Shield, our SEM had substantial explanatory power across the region (χ251 = 45.37, p-value = 0.70, R2 = 0.75). Nitrate concentrations (p-value <0.001), water temperature (p-value = 0.002), and the latent watershed characteristic (p-value <0.001) had a positive influence on MeHg concentrations once variable interactions were accounted. Due to the inherent strengths of applying an SEM approach, we describe two plausible pathways driving MeHg concentrations: 1) indirect effect of forest-derived nutrients increases in-situ MeHg production in Dryden – Wabigoon Forest streams, and 2) direct supply of MeHg from inundated soils following consistent precipitation and inundation events (i.e., fill, sit, and spill).
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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 | 0.001 |
| 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 | 0.001 |
| 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".