Impacts of Mercury Exposure on Human Health, Safety and Environment: Literature Review and Bibliometric Analysis (1995 to 2021)
Bibliographic record
Abstract
Introduction: Mercury is a highly toxic and persistent contaminant found in food and parts of the environment. Over the years, global research on mercury poison has soared owing to concerns about its effects on human health, occupational safety, and environmental sustainability. Although numerous studies have identified and examined the various types, sources, toxicity, exposure, and impacts of mercury, comprehensive studies on the research landscape and scientific developments on the subject areas are currently lacking. Therefore, this paper shows a bibliometric analysis (BA) and literature review (LR) of the top publications, funders, organisations, and countries working on Mercury research worldwide. Methods: The research landscape on the subject area was examined by BA from 1995 to 2021, whereas the scientific developments were highlighted through LR. Results: Results showed that mercury research has gained global prominence since the discovery of the Minamata disease in 1956. The most prolific mercury researchers, institutions, and funders are from the United States, Japan, Brazil, Canada, and China, whereas the publications on Mercury research doubled over the period. The top source titles for publications on Mercury are Neurotoxicology, Science of the Total Environment, and Environmental Health Perspectives. However, Micheal Aschner (US) and Takashi Yorifuji (Japan) are the most prolific researchers. Co-occurrence analysis revealed that mercury, methyl mercury, fish, toxicity, and Minamata disease are the most cited keywords, which shows the correlation nexus between fish consumption and mercury poisoning. Conclusion: The LR showed that mercury research is widely investigated due to global concerns about its impact on human health, safety, and the environment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.146 | 0.214 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".