Integrated Plankton and Trace Metal Analysis as Indicators of Organic Pollution Threats from Carp-Cultured Ponds to Adjacent Ecosystems: Implications for Sustainability
Bibliographic record
Abstract
Abstract Stawy Milickie (Milicz Ponds) is a natural reserve that has a great ecological importance as a Ramsar site and economic importance as one of the largest carp breeding centres in Europe. Continuous anthropogenic pressure on these large ecosystems can lead to a high disturbance on its natural ecology and sustainability. Phytoplankton, one of the most important indicators of any anthropogenic load, especially organic pollution have been studied in the present study. This is the first work ever from Milicz ponds, wherein the diversity, community structure and seasonal variability of phytoplankton with respect to changes in physicochemical parameters and trace metals have been studied. Periodic samples of water and phytoplankton were collected from four selected ponds of various sizes from October 2018 to September 2019. Of the four selected ponds one was unused for carp cultivation. The study reports the presence of 147 phytoplankton species in nine distinct taxonomic groups. The Genera based Algal pollution index indicated presence of organic pollution in both noncultured pond and carp-cultured pond. The heavy metals detected in the concentration order of Cd > Mn > Fe > Zn > Cu > Pb remained much higher than the safe limits of freshwater aquaculture ponds. The probable sources of these pollutants were identified as artificial feed used in the cultivation of carp and inundation from the river basin. This study emphasizes the urgent need for continuous qualitative and quantitative assessment and monitoring of water parameters, including plankton, in these ponds. This study highlights how the structure of microplankton communities, in conjunction with heavy metal analysis, serves as indicators of the spread of organic pollution in non-cultivated aquaculture environments. It strongly advocates for implementing sustainable measures to safeguard this vital ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".