The accumulation of Cu, Zn, Cd, and Pb in the aquatic biomass of sulphide tailing ponds
Bibliographic record
Abstract
Flooding mine tailings to limit the oxidation of sulfides provides a habitat for aquatic organisms, such as plants, plankton, insects, and fish, which can uptake metals and, thus, threats for local ecosystems and influence the cycling of elements in biogeocenosis. An aquatic ecosystem developed naturally in sulphide tailing ponds containing cyanidation wastes of the Salair ore-refining plant (SORP), Russia, was studied. The objectives of this research were to: (i) reveal the level of contamination of living organisms in the tailing ponds compared to a natural control site and (ii) calculate the weight of metals in aquatic biomass to estimate the amount of metals transferring from the tailing ponds into the biogechemical cycle. The concentration of Cu, Zn, Cd, and Pb in the sediments of the tailing ponds is significantly higher than from the control site. Concentrations of Cu, Zn, Cd, and Pb in plant shoots were significantly higher than in the control and accumulated mainly in cell envelopes and membranes. The concentration of Pb in fish liver and eggs were 41 and 7.5 times higher, respectively, than maximum allowable concentrations. The biomass distribution between producers and consumers of the tailing pond ecosystem is similar to those of natural pond ecosystems. However, the weights of Cu, Zn, Cd, and Pb in all trophic levels per hectare of the tailing pond are orders of magnitude higher than those for Lake Baikal. The largest portion of metal circulates within the ecosystem of the Dyukov Ravine Pond with a maximum of 5 to 13% of this amount transferred into the surrounding environment through the food chains.
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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 | 0.001 | 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".