Metal concentrations in fish tissues in the Kara, Bolshoi Patok, and Maly Patok River basins, North-Eastern European Russia
Bibliographic record
Abstract
Abstract Fish and fish tissue are effective bioindicators due to their sensitivity to pollution and are frequently used for assessing aquatic ecosystem health. Establishing baseline metal concentrations in freshwater fish tissues within aquatic ecosystems is important prior to establishing industrial activities to help determine potential future industrial impacts. Historically, North-Eastern European Russia has been an area with relatively low levels of industrial development and is still in pristine condition. In this region the noise to background ratio for industrial contaminants may be disproportionately high. This study measured baseline metal concentrations in freshwater fish tissues collected from three study sites (Bolshoi Patok, Maly Patok and Kara River) in North-Eastern European Russia as bioindicators for overall aquatic ecosystem health. Seven fish species including European Grayling, Arctic Char, Whitefish, Perch, Pike, Roach, and Peled over a five-year period between 2000 to 2005. Fish tissue samples were analyzed for Copper (Cu), Lead (Pb), Cadmium (Cd), and Zinc (Zn) metal concentrations. Metal concentrations measured in fish tissues in this study compared favourably to remote sites in Alaska in the US and Slovenia. Despite small variation between sampling sites, metal concentrations were relatively low and considered in pristine condition. Metal concentrations measured in fish tissues in this study represent baseline conditions which will be important to compare against using monitoring programs should the region experience future industrial development.
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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".