Seasonally and regionally determined indication potential of bioassays in contaminated river sediments
Bibliographic record
Abstract
River sediments are a dynamic system, especially in areas where floods occur frequently. In the present study, an integrative approach is used to investigate the seasonal and spatial dynamics of contamination of sediments from a regularly flooded industrial area in the Czech Republic, which presents a suitable model ecosystem for pollutant distribution research at a regional level. Surface sediments were sampled repeatedly to represent two different hydrological situations: spring (after the peak of high flow) and autumn (after longer period of low flow). Samples were characterized for abiotic parameters and concentrations of priority organic pollutants. Toxicity was assessed by Microtox test; genotoxicity by SOS-chromotest and green fluorescent protein (GFP)-yeast test; and the presence of compounds with specific mode of action by in vitro bioassays for dioxin-like activity, anti-/androgenicity, and anti-/estrogenicity. Distribution of organic contaminants varied among regions and seasonally. Although the results of Microtox and genotoxicity tests were relatively inconclusive, all other specific bioassays led to statistically significant regional and seasonal differences in profiles and allowed clear separation of upstream and downstream regions. The outcomes of these bioassays indicated an association with concentrations of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) as master variables. There were significant interrelations among dioxin-like activity, antiandrogenicity and content of organic carbon, clay, and concentration of PAHs and PCBs, which documents the significance of abiotic factors in accumulation of pollutants. The study demonstrates the strength of the specific bioassays in indicating the changes in contamination and emphasizes the crucial role of a well-designed sampling plan, in which both spatial and temporal dynamics should be taken into account, for the correct interpretations of information in risk assessments.
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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 | 0.000 | 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) | 0.000 | 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".