Impact of Land Use/Land Cover and Landscape Pattern on Water Quality in Dianchi Lake Basin, Southwest of China
Bibliographic record
Abstract
The water quality of a basin is pronouncedly affected by the surrounding types of land use. Analyzing the impact of LULC and landscape patterns on water quality is critical for identifying potential drivers. To further study how LUCC affects the water quality in a typical plateau lake basin, this study investigated the impacts of land-use types on water quality in the Dianchi Lake Basin in Southwest China. We analyzed changes in land-use types and the landscape pattern of the Dianchi basin, calculated the CWQI (Canadian Water Quality Index) value based on the water quality indexes (PH, total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (COD), dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical ox-ygen demand (BOD5), ammonia nitrogen (NH3-N), turbitidy, and chlorophyll-a (Chla)), used the RDA (Redundancy Analysis) and SMLR (Stepwise multiple linear regression) methods, the coupling degree, coupling coordination degree, and the geographical detector model to explore the relationship between water quality and changes in the land-use type. The results show that (1) changes in the land-use types were obvious: the majority of the land, which was originally forest land, became built land in 2020 and farmland in 1990 (except for the Dianchi water). Landscape pattern indexes indicated that almost all land-use types were first scattered, then gathered from 1990 to 2020. (2) Changes in the water quality of Dianchi Lake lagged behind the changes in land-use types, and the variation trends were similar to the landscape pattern variation trends. The CWQI value decreased in a nearly linear fashion from 1990 to 1998, exhibited a slight change from 1999 to 2013, and quickly increased from 2013. (3) Land-use types demonstrated a tight correlation with the Dianchi water quality, and LPI was the most dominant factor in both Caohai Lake and Waihai Lake. (4) There were different indexes affecting the coupling coordination degrees of Caohai Lake and Waihai Lake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".