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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".