Environmental Risk for the Freshwater Ecosystem of the Yenisei River with Consequences for Human Health Risks
Bibliographic record
Abstract
The long-term monitoring of the state of the freshwater ecosystem of the River Yenisei revealed the statistically reliable content of heavy metals (Fe, Zn, Cd, Cu, U, etc.) in the water, bottom sediments, phyto- and zoo-plankton, and muscle mass of commercial fish (benthos eaters, predators and herbivorous fish) consuming different types of food. The values of the indices of the ecological state of the Yenisei River were estimated to vary from 2.38 to 2.85. The total index of risk for the water, considering the reference doses, amounts to 0.16 for the water, and to 0.47 for the flesh of commercial fish. The total index of risk for the population consuming freshwater and fish from the Yenisei River amounts to IR=0.63. The obtained value of the index is, in general, of no danger for the population health. Though the carcinogenic substances were not accurately revealed, non-carcinogenic substances were estimated to the level of non-threshold risks. The non-threshold risks of non-carcinogenic substances was found 0.017, far lower than permissible limit 0.050. The ratio of reflectory-olfactory effects and total non-carcinogenic risk was found, respectively, 0.01 and 0.34. The integrated indictor was 0.35, which did not exceed the regulatory level (II≤1). Conclusively, the risks associated to various analyzed indicators did not exceed the permissible levels and did not require additional measures of monitoring the water quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".