Current State of Lake Kotokel (Eastern Cisbaikalia, Russia): Hydrochemical Characteristics, Water Quality, and Trophic Status
Bibliographic record
Abstract
Lake Kotokel, the largest lake on the eastern shore of Lake Baikal, has historically served as an important fishery and recreational resource. However, it underwent an ecological crisis and a Haff disease outbreak in 2008–2009. Hydraulic engineering interventions were subsequently implemented, and the lake was closed to tourism and fishing for an extended period. This study provides the first comprehensive analysis of Lake Kotokel’s water level fluctuations from 1985 to 2022 and evaluates hydrochemical data collected between 2015 and 2024. A comparative assessment of the seasonal variability in Lake Kotokel’s condition during 2023–2024 and 2008–2009 was conducted using various water quality indices, including the Russian Specific Combinatorial Water Pollution Index (SCWPI) and Basic Anthropogenic Load Index (ALI), as well as the international National Sanitation Foundation Water Quality Index (NSF-WQI) and Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI). Trophic state indices, such as Carlson’s Trophic State Index (CTSI) and the Trophic Index (TRIX), were also applied. The analysis revealed a seasonal decline in water quality, transitioning from pure (“excellent”) and “light eutrophic” index classifications in spring to polluted (“marginal”) and “hypertrophic” index classifications in summer and autumn. This study demonstrated that a combination of unfavorable factors, including significant lake-level fluctuations, prolonged high temperatures during the vegetative period, and the discharge of fracture-vein waters, led to a sharp decline in water quality and an increase in the lake’s trophic status. Elevated levels of iron, manganese, COD, pH, and ammonium detected in water samples in 2024, alongside incidents of fry mortality in spring and summer and intense algal blooms, raise concerns as they may signal a potential recurrence of Haff disease in the lake.
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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 | 0.001 |
| 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".