Assessment of temporal trends of water quality in agricultural watersheds of Jeonnam Province using the CCME-WQI
Bibliographic record
Abstract
This study investigated seasonal water quality characteristics and changes in agricultural watersheds of Jeollanam-do from 2021 to 2024 and analyzed their relationships with farmland proportion and precipitation using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI).The results showed that during periods of high rainfall, concentrations of COD and SS significantly increased, and water quality deterioration was more pronounced in areas with higher farmland proportions.Strong negative correlations were observed between CCME-WQI and key water quality indicators such as COD, T-N, and T-P, indicating their significant contribution to water quality degradation.These findings suggest that CCME-WQI is a suitable tool for integrated water quality assessment in agricultural watersheds and may can serve as a basis for analyzing pollution sources and establishing effective management strategies under changing agricultural environments.For sustainable water quality management, further investigation of non-point pollution sources and long-term monitoring 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".