Earthworms as bioindicators of mercury pollution from mining and other industrial activities
Bibliographic record
Abstract
Mercury (Hg) can be released into the environment by various natural and industrial processes. Given the potential for environmental discharges from a number of sources and the severity of hazards associated with this highly toxic metal, potential mercury transformations must be well understood to effectively predict and prevent harmful human and environmental health effects. Bioindicators play an important role in identifying the factors controlling Hg toxicity and bioavailability and can ultimately be used to evaluate hazardous situations. A methodology using the earthworm Eisenia foetida has been developed to assess Hg bioavailability in mine tailings and aqueous solutions. Results indicate that E. foetida accumulate Hg and a positive correlation exists between Hg concentrations in worm tissues, the substrate they consume and length of exposure. To investigate the effect of natural organic acids on Hg bioavailability, metallic Hg (Hg 0 ) was dissolved in tannic acid and ‘fed’ to the worms in a substrate of paper and silica sand. Total Hg and methylmercury (MeHg) were analysed to determine whether methylation of Hg was occurring in the substrate, directly within worm intestines, or in the tannic acid–Hg solution. The MeHg:total Hg ratio was up to 160 times higher in worm tissues than both the tannic acid–Hg solution and the substrate. This result is particularly significant in organic-rich systems, where naturally occurring organic acids may be facilitating methylation within organisms digestive tracts.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".