Concentration of Chemical Elements in the Leaves of the <i>Salix miyabeana</i> Seemen, Growing in the Area of the Tailings Dam of the Darasun Gold Deposit
Bibliographic record
Abstract
In the area of the tailings dam of the Darasun gold deposit in the Trans-Baikal Territory, the content of 47 chemical elements in the leaves of the Miabe willow (Salix miyabeana), as well as their gross content in the soil in places where plants grow, were studied to obtain information about the accumulation of elements by the plant on contaminated soils and the prospect of using Miabe willow as a phytoextractor plant. The analysis of plant and soil samples was carried out on an ICP-MS Elan 9000 mass spectrophotometer (Canada). The method of measuring the metal content in solid objects by the ISP-MS method was used. It was found that the gross content of Ag, Pb, Cd, Cu, Zn, W, Hg, B and especially Te, Bi, As and Sb in the soil was 2–840 times higher than the clark of the Earth’s crust. The total content of As, Zn, Pb, Sb and Cd in the soil was 1.3–7.0 times higher than the maximum permissible concentrations (MPC) and approximately permissible concentrations (APC) of chemicals, and the arsenic content exceeded the established limit by 240 times. The concentration of K, Sr, Ti, P, Zn, Ag, As and Cd in the leaves of the Miabe willow exceeded the clark of terrestrial plants by 1.5–3.0 times. A correlation was found between the concentration of Cd, Zn, B, Mn, Be, Ga and V in the leaves of the Miabe willow with the gross content of these elements in the soil of the plant’s growing sites. The storage elements in the plant were Se, P, Cd, Zn, B and K. The coefficient of biological accumulation of Se ranged from 1 to 40, Cd – 1.1–5.8, Zn – 0.5–2.6. Miabe willow is a promising plant for extracting cadmium and zinc from contaminated soils.
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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".