Multiple factor analysis using water quality index scores and parameters as an approach for evaluating the environmental status of polluted lakes along the Black Sea coast of Bulgaria
Bibliographic record
Abstract
The moderately salty and lightly salty lakes and marshes near the Black Sea are specific in terms of their high degree of physical alteration; intensive hydromorphological pressure; and point-source and diffusive enrichment with biogenic, organic and inorganic compounds. Nutrients are among the most regularly measured variables in monitoring programs, providing the most complete information for long-term analysis and assessment. Nonetheless, their results need a final summary score, such as the water quality index, which assesses spatial and temporal conditions very well. In this study, we used all available data for Varna and Burgas Lakes from state monitoring for six years (2016–2021), using the parameters monitored with the greatest frequency. The aims were to trace temporal changes in the water quality parameters to determine which of the biogenic elements had the greatest significance for the variance in water quality while seeking the most contributing elements for the formation of the Canadian Council of Ministers of the Environment water quality index (CCME-WQI). The objectives were achieved via multiple factor analysis (MFA) loaded with the results for the environmental variables and the final scores of the CCME-WQI since this multivariate analysis allows simultaneous consideration of multiple data series while balancing the influence of each set of variables. MFA revealed that CCME-WQI scores were influenced solely by total phosphorus (TP) in Varna Lake, where TP was negatively correlated with total nitrogen. In Burgas Lake, TP had the greatest influence on the CCME-WQI, but in this slightly saline lake, pH and dissolved oxygen were also negatively correlated with the complex assessment scores. The approach developed in this study is simple to implement and provides information for the simultaneous use of both the CCME-WQI and the MFA, which could optimize monitoring programs by directing sampling efforts on fewer parameters that could be analyzed more often or from more sampling sites.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".