Evaluation of long-term water quality trends and CCME-WQI applicability in agricultural watersheds of Korea
Bibliographic record
Abstract
This study analyzed 10-year (2015 -2024) trends in water quality parameters and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) across five provinces in Korea.The CCME-WQI analysis indicated that Gangwon-do maintained Fair to Good ratings with high stability, while Gyeonggi-do generally fell within the Marginal range.Chungcheong-do showed persistently low water quality, mostly within the Poor range.Jeolla-do exhibited mostly Poor-level quality, and Gyeongsang-do showed moderate variability with Fair-level quality overall.The nationwide average CCME-WQI was approximately 55.7, indicating a marginal water quality level overall.The coefficient of variation (CV) for CCME-WQI was consistently lower than that of individual parameters, supporting its robustness and representativeness as an integrated indicator.Correlation analysis revealed strong positive relationships between farmland ratio and nutrient-related indicators (e.g., COD Mn , T-P, SS), and significant negative correlations between CCME-WQI and these pollutants (e.g., SS: r = -0.96).Additionally, cumulative precipitation-particularly in Julyshowed stronger negative correlations with CCME-WQI (e.g., July: r = -0.80)than the farmland ratio (r = -0.69),suggesting that hydrological variability may exert a more immediate influence on water quality in agricultural watersheds than static land use patterns.These findings suggest that CCME-WQI is an effective tool for long-term water quality evaluation in agricultural watersheds influenced by non-point source pollution.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".