Assessing water quality for urban tributaries of the Three Gorges Reservoir, China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Water quality assessment is essential for water resources management. This paper presents a comprehensive evaluation of water quality conditions in three urban tributaries of the Three Gorges Reservoir, China. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) and Nemerow Pollution Index (NPI) approach were used in this study. Generally, the assessment results of the NPI approach are consistent with that of the CCME-WQI approach. However, the NPI method overemphasized the influence of the most serious pollutant factor, and thus this method should be used with caution for water resources managers. The CCME-WQI values indicated that the water quality conditions in the Wubu River were quite good during the period 2013–2015. Water quality conditions in the upstream sections of Yipin and Huaxi River are good. However, when the river drains through urban areas, water quality conditions greatly deteriorate due to the excessive release of household and municipal sewage, and industrial wastewater, especially for Huaxi River. Thus, waste water management becomes more and more imperative in urban regions of China. Meanwhile, assessment results indicate that the CCME-WQI approach can provide a reference for decision-makers on water resources management.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it