Concentrations of metals and trace elements in different tissues of nine fish species from the Medjuvrsje reservoir (West Morava River Basin, Serbia)
Bibliographic record
Abstract
Element concentrations in selected fish species from different trophic levels were analyzed. The following fish species were analyzed: common nase (Chondrostoma nasus), roach (Rutilus rutilus), freshwater bream (Abramis brama), barbel (Barbus barbus), Prussian carp (Carassius gibelio),chub (Squalius cephalus), European perch (Perca fluviatilis), wels catfish (Silurus glanis)and northern pike (Esox lucius). Fish were collected from the Medjuvrsje Reservoir (West Morava River Basin, western Serbia) during 2012, and samples of liver, muscle and gills were analyzed for As, B, Ba, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Mo, Ni, Pb, Sr, and Zn using inductively coupled plasma optical emission spectrometry (ICP-OES). The liver and gills had the highest measured element concentrations, with Cu, Zn, Fe and Sr being the most prominent. The bioaccumulation of metals was species-specific, with the accumulation exhibiting the following trends: Prussian carp > northern pike > freshwater bream > European perch > chub > common nase > barbell > roach > wels catfish; Li>Pb>Zn>Cu>Fe>Ba>Mn>Sr>Cr>As>Mo>Ni>B, in all examined species; for Co, Cd and Hg, the bioaccumulation factor (BAF) was 0. Results of this study point to the tissue-specific differences in element concentrations, and to distinct differences between fish species regarding the accumulation patterns: common nase, with the highest accumulation observed in the liver, and Prussian carp, with the highest accumulation observed in the gills.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".