Physicochemical characterization and presence of heavy metals in the trout farming area of Lake Titicaca, Peru
Bibliographic record
Abstract
Certain areas of Lake Titicaca exhibit signs of contamination from urban drainage, mining tailings, and waste from trout cage farming. The objective of this study was to determine the physicochemical conditions of the water and the concentration of heavy metals in sediments of a trout (Oncorhynchus mykiss) farming area in Puno Bay, Lake Titicaca. Water samples were collected at depths of 1, 5, 10, and 15 meters from the surface, as well as from sediments at the bottom of the lake. Additionally, samples were taken 500 meters from the trout breeding area, where no farming activities took place. The study was carried out over a period of 10 months. Some physical-chemical measurements were taken in situ using a multiparametric device, while others were carried out at IMARPE’s laboratory. The results indicate that the physical-chemical quality of the water does not exceed the tolerance limits recommended by the Environmental Quality Standards - ECA Peru. The water pH was 8.79 and the dissolved oxygen was 6.81. The heavy metals (Hg, Cd, Pb, Zn, Cu) found in the sediments were within the permissible limits compared to the ISQG Canada Standard. However, the concentration of As, at 41 mg kg-1, exceeded the tolerance limit. Uncontrolled trends in trout production volumes could compromise water quality and sustainability.
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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 | 0.000 |
| 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".