The Effects of Forest Harvesting on Total and Methylmercury Concentrations in Surface Waters Depend on Harvest Practices and Physical Site Characteristics
Bibliographic record
Abstract
Forest harvesting can lead to mercury (Hg) mobilization from soils to aquatic habitats and promote the transformation of inorganic Hg to highly neurotoxic and bioaccumulative methyl-Hg (MeHg). Multiple past studies reveal broad variation of stream water MeHg and total Hg (THg) concentration responses to forest harvesting, which has confounded messaging to forest and resource managers. To advance beyond divergent and sometimes contradictory findings, we synthesized information for 23 previously studied catchments in North America and Fennoscandia and compiled a uniform set of soil, landscape, and harvesting properties to identify forest management, riparian, and hillslope factors that influence responses of stream water MeHg and THg concentrations. From this synthesis, we found catchments with high soil moisture and organic soil layers >100 cm to be at highest risk for disturbance-induced increases in MeHg formation after harvest but not necessarily affecting concentrations of MeHg in stream waters. Instead, the combination of MeHg formation in soils along with factors that affect mobilization with runoff to streams most influenced how forest harvest affects MeHg concentrations in stream waters.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".