Characteristics of Dissolved Organic Matter and Its Relationship with Water Quality along the Downstream of the Kaidu River in China
Bibliographic record
Abstract
The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the characteristics of dissolved organic matter (DOM) along the river flow, and its relationship with water quality, were analyzed using the Canadian water quality index (CWQI), parallel factor (PARAFAC) and self-organizing map (SOM). The study results include: (1) The conclusion of field sampling along the lower reaches of the Kaidu River and laboratory measurements of water quality parameters, using CWQI to determine the water quality index of the lower Kaidu River, ranging between 59.58 and 93.47. The water quality of the lower reaches of the Kaidu River generally ranges between moderate and good, and can meet the water use requirements of Class II water function standards. (2) The DOM composition of the river predominantly contained three fluorescence components, while the three fluorescence indices of the water body varied less in different river sections. Based on the SOM training model, the fluorescence intensity of the C1 component was larger among the three fluorescence components, followed by the C2 component, and the smallest fluorescence intensity of the C3, which was dominated by humic-like substances, with a high authigenic origin and humification degree. (3) The fluorescence index and fluorescence components were correlated with water quality parameters, and it was found that C1, C2 and C3 were negative and correlated significantly with SO42- and Total-dissolved solids (TDS) concentrations; FI, HIX and BIX showed strong positive correlations with SAL and Cu and negative correlations with dissolved oxygen (DO). This study provides a scientific basis for surface water quality monitoring and water quality pollution management in the Kaidu River.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".