Concentration of Heavy Metals in the Biot of Lake Radoniqi and Badovci, Food Safety: Study of the Natural Environment in the Republic of Kosovo
Bibliographic record
Abstract
The purpose of this research is to determine the content of heavy metals in the lakes of Kosovo, lakes with heavy metals such as: (Hg, Cd, Pb, As, Fe, Zn, Ni, Cu), in water and water sediment in Lake Radoniq and Lake Badovc. Determination of heavy metals (Pb, Cd, Hg, As), in muscle tissue and fish organs. Parameters as an indicator of fish safety for food, indicator of environmental pollution (Kosovo lake bioten). Age of fish, the amount of fat are important factors that affect the accumulation of heavy metals in fish. This indicates that the bioaccumulation of heavy metals is a special process and indicates the concentration of heavy metals in the body of the fish. The high concentration of Fe in fish organs is of particular importance for hemoglobin and its role in fish. Metal indicators such as biocumulation factors are different, for example, for Pb can be increased with high concentrations compared to international parameters which depends on the species of fish and the location of catching s137 fish, the concentration of heavy metals in the Lake and the impacts from agricultural activities. The concentration of heavy metals in the body of aquatic life depends on the way in which heavy metals penetrate, giving the body the opportunity to detoxify them through metabolism. Metabolism means the exchange of substances, the uninterrupted exchange of matter between the living organism and the external environment. This process is the basis of life, which allows a cell to grow and reproduce, maintain its structure and respond to its environment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".