Modulators of mercury risk to wildlife and humans in the context of rapid global change
Bibliographic record
Abstract
Environmental mercury (Hg) contamination is an urgent global health threat. The complexity of Hg in the environment can hinder accurate determination of ecological and human health risks, particularly within the context of the rapid global changes that are altering many ecological processes, socioeconomic patterns, and other factors like infectious disease incidence, which can affect Hg exposures and health outcomes. However, the success of global Hg-reduction efforts depends on accurate assessments of their effectiveness in reducing health risks. In this paper, we examine the role that key extrinsic and intrinsic drivers play on several aspects of Hg risk to humans and organisms in the environment. We do so within three key domains of ecological and human health risk. First, we examine how extrinsic global change drivers influence pathways of Hg bioaccumulation and biomagnification through food webs. Next, we describe how extrinsic socioeconomic drivers at a global scale, and intrinsic individual-level drivers, influence human Hg exposure. Finally, we address how the adverse health effects of Hg in humans and wildlife are modulated by a range of extrinsic and intrinsic drivers within the context of rapid global change. Incorporating components of these three domains into research and monitoring will facilitate a more holistic understanding of how ecological and societal drivers interact to influence Hg health risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".