Mercury exposed: Advances in environmental analysis and ecotoxicology of a highly toxic metal
Bibliographic record
Abstract
Concerns about environmental mercury pollution stem largely from the adverse effects of biotic exposure to methylmercury, a highly toxic compound that readily crosses biological membranes, accumulates in exposed organisms, and biomagnifies in food webs [1][2][3].Most of the mercury in anthropogenic and natural emissions, air, wet and dry atmospheric deposition, soils, and oxic surface water exists as inorganic forms.Methylmercury is produced in the environment by methylating bacteria [4,5], and the microbial methylation of inorganic mercury is a key process affecting the amount of methylmercury bioaccumulated and biomagnified in food webs [1,6].Thus, methylmercury concentrations are greatest in aquatic, wetland, and terrestrial environments that have conditions conducive to microbial methylation of inorganic mercury.The primary pathway of human exposure to methylmercury is consumption of estuarine, marine, and freshwater fish [7-9], and processes that affect the mass of methylmercury in aquatic ecosystems or its concentration at the base of aquatic food webs strongly affect its concentration in all trophic levels, including fish [6,10,11].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".