Water quality analysis using the CCME-WQI method with time series analysis in a water supply reservoir
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The quality of the drinking water source reservoirs has always been a research hotspot. However, few have studies focused on the water quality of reservoirs over a relatively long period with time series analysis. In this paper, based on water quality and hydrological data from 2010 to 2020, considering 8 water quality parameters, CCME-WQI with time series analysis was used to explore the interannual and seasonal changes in water quality in the Weishui Reservoir. Furthermore, the main factors affecting water quality were discussed through correlation analysis. The ARIMA model is used to predict water quality in the future. The results show that the water quality was seriously polluted from 2012 to 2013. After 2018, the water quality gradually improved and stabilized. In addition, the water quality is affected by inflow, showing the characteristics of poor water quality in summer and winter. The key parameters affecting water quality are TN and TP, which are almost 2 times higher than the grade II standard of water quality standard. Through the ARIMA model, it is predicted that CCME-WQI is maintained at 80.46 indicating that the water quality will be stable in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 it