Global mercury concentrations in biota: their use as a basis for a global biomonitoring framework
Bibliographic record
Abstract
Abstract An important provision of the Minamata Convention on Mercury is to monitor and evaluate the effectiveness of the adopted measures and its implementation. Here, we describe for the first time currently available biotic mercury (Hg) data on a global scale to improve the understanding of global efforts to reduce the impact of Hg pollution on people and the environment. Data from the peer-reviewed literature were compiled in the Global Biotic Mercury Synthesis (GBMS) database (>550,000 data points). These data provide a foundation for establishing a biomonitoring framework needed to track Hg concentrations in biota globally. We describe Hg exposure in the taxa identified by the Minamata Convention: fish, sea turtles, birds, and marine mammals. Based on the GBMS database, Hg concentrations are presented at relevant geographic scales for continents and oceanic basins. We identify some effective regional templates for monitoring methylmercury (MeHg) availability in the environment, but overall illustrate that there is a general lack of regional biomonitoring initiatives around the world, especially in Africa, Australia, Indo-Pacific, Middle East, and South Atlantic and Pacific Oceans. Temporal trend data for Hg in biota are generally limited. Ecologically sensitive sites (where biota have above average MeHg tissue concentrations) have been identified throughout the world. Efforts to model and quantify ecosystem sensitivity locally, regionally, and globally could help establish effective and efficient biomonitoring programs. We present a framework for a global Hg biomonitoring network that includes a three-step continental and oceanic approach to integrate existing biomonitoring efforts and prioritize filling regional data gaps linked with key Hg sources. We describe a standardized approach that builds on an evidence-based evaluation to assess the Minamata Convention's progress to reduce the impact of global Hg pollution on people and the environment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".