ASSESSMENT OF RIVER WATER QUALITY IN THE CITY BY HYDROCHEMICAL INDICES (THE OKHTA RIVER, ST. PETERSBURG)
Bibliographic record
Abstract
The Okhta River water quality was assessed in the period 2016–2020 using the component- wise assessment method and three hydrochemical indices (Water Pollution Index (WPI), Specific Combinatorial Water Pollution Index (SCWPI), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)). The component-wise assessment demonstrated that the pH was neutral or slightly alkaline, and water hardness was low. Dissolved oxygen deficit was observed at most stations during the entire research period. The content of dissolved oxygen declined downstream along the river. At the river mouth, the oxygen situation is slightly better due to the inflow of the Neva River water. The BOD5 exceeded the MPC at all sampling stations, suggesting the Okhta River water was polluted with readily oxidizable organic matter. The iron content in the water exceeded the MPC manifold. An elevated content of various forms of nitrogen was also revealed. Over the entire observation period, increased concentrations of oil products were constantly recorded (exceeding MPC more than 50‑fold in different years). The Okhta River water was characterized as “dirty” and “extremely dirty”, and the water quality was “poor” according to the calculated values of the indices – WPI, SCWPI and CCME WQI. The methodology of the little-known in Russia CCME WQI is considered separately. It was compared with the WPI and SCWPI. The relationship between the indices is clarified. The use of CCME WQI for surface water quality assessment along with SCWPI is recommended.
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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".