Миграция селена в водной экосистеме реки Волги в границах Саратовской области
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The content of selenium was determined in the waters of the Volga river, in bottom ground, in the macrophytes, in plankton and benthic forms, mollusks and organism of different fish species. The highest concentration of selenium was recorded in the districts of the ravines Tokmakovsky and Nazarovsky within the boundaries of Saratov, the Saratov region. The concentration of the microelement on the right bank was slightly higher than on the left one concentration of selenium in ground soil varied within 0.062-0.091 mkg/g: in the Left bank soil 0.062-0.090 mkg/g, in the Right bank soil 0.068-0.091 mkg/g. In accordance with the average value of selenium macrophytes can be arranged in the following order (reduction): pondweed perfoliate, Canadian waterweed, broadleaf and semi-submerged cattail. Planktonic and benthic forms must be regarded as the most important links in the food chain of reservoirs, which play a huge role in the concentration and biogenic migration of selenium. The highest concentration of microelements is fixed in oligochaetes (0.042 mkg/g) and the smallest in the body of amphipods (0.039 mkg/g). In accordance with the average concentration of selenium the species of mollusks can be placed in the following order: mussel (0.044 mkg/g), pearl shell (0.042 mkg/g), pond snail (0.041 mkg/g) and river zebra mussel (0.040 mkg/g). The concentration of selenium in fish depends on the type of the food. By the ability to accumulate selenium the studied species of freshwater fish can be placed in the following order: crucian carp (0.079 mkg/g); silver carp (0.073 mkg/g); titmouses (0.072 mkg/g); sazan (0.069 mkg/g), and rudd (0.068 mkg/g). The results of the study help draw a conclusion that the concentration of selenium in water of the river Volga is not uniform and different natural and human factors influence the content of microelement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.075 |
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 it