Spatio-Temporal Variation of Water Quality in Lixia River Watershed Associated with the Operation of a Water Diversion Project
Bibliographic record
Abstract
Abstract Based on multi-variable water quality data, spatio-temporal variations of water quality can be explored to provide a scientific basis for sustainable water quality management. Lixia River Watershed, a relatively closed low-lying polder area in the lower reaches of Huai River basin, China, is closely related to the Yangtze River system. The spatio-temporal variations of water quality of Taizhou, a subsection of the Lixia River Watershed, were assessed based on the monthly water quality data of 39 sampling sites in 2017, by combining methods of Water Quality Index of the Canadian Council of Ministers of the Environment (CCME WQI), cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA). The results show that CCME WQI ranged from 70.17 to 95.35, and the water quality of sampling sites could be grouped into 3 categories, where the southwestern parts of both Hailing and Jiangyan were excellent, the south and northeast of Xinghua were good, the southeast of Jiangyan and the northwest of Xinghua were fair. The 12 months of 2017 were grouped into two clusters, water quality in time period 1 (January to March, July to October) was poor, while that in time period 2 (April to June, November to December) was good. Sampling sites were grouped into two clusters: spatial group A (southwest of both Jiangyan and Hailing, south of Xinghua) and spatial group B (southeast of Jiangyan, north of Xinghua), and spatial group B was seriously polluted. NH3-N, total phosphorus (TP) and five-day biochemical oxygen demand (BOD5) were the major variables responsible for water quality variation, while permanganate index (CODMn), chemical oxygen demand (CODcr) and dissolved oxygen (DO) were the secondary parameters.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".