Biogeochemical cycle of mercury and controlling technologies: Publications in critical reviews in environmental science & technology in the period of 2017–2021
Bibliographic record
Abstract
The Minamata Convention on Mercury (Hg) calls for global efforts to reduce the release and risk of Hg. A better understanding of the global Hg budget, transformation and transport as well as toxicity of Hg in the environment, and the Hg emission controlling technologies, is important to minimize Hg pollution and exposure risks. Here, we summarized recent findings regarding the Hg cycle, transport, transformation, and controlling technologies, based on publications in Critical Review in Environmental Science and Technology (CREST) during 2017–2021. In terms of CREST publications we first focused on the biogeochemical cycle and impacts of Hg in the sensitive environment of the Tibet Plateau, and technologies being used to control Hg0 emission from thermal power plants. Second, we discussed the roles of forest in the global cycle of Hg. Third, we reviewed the transport of Hg at artisanal and small-scale gold mining sites, and the mobility and transformation of Hg species in the environment. This special issue covers the recent studies on the cycle, transport, and transformation of Hg, enhances our abilities to develop better strategies to minimize its risks. There are emerging concerns with climatic change and natural and human perturbations on the biogeochemical cycle of Hg in the environment and futures studies are warranted in these areas as well as the global Hg cycle, transport, and remediation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".