Spatial and Temporal Change Monitoring of Wetland Urban Ecology Based on a Remote Sensing Ecological Index Considering Full Elements
Bibliographic record
Abstract
Influenced by the exploitation of natural resources and industrialization, ecological and environmental problems have become increasingly severe worldwide, particularly in rapidly developing countries such as China. This study utilizes Earth observation satellite data to monitor changes in ecological environment quality of the Wuhan Urban Development Zone (WUDZ) from 2000 to 2020, employing the remote sensing ecological index considering full elements. By incorporating water bodies into the calculation through the entropy weight method and moving window, this approach takes into account the benefits of water elements on the overall ecological environment (EE). The results indicate the following: 1) from 2000 to 2020, the overall EE of WUDZ exhibited an initial improvement followed by a subsequent decline, with minor fluctuations. 2) The EE of WUDZ is dominated by greenness and dryness. The central and main urban areas have poorer ecological environment quality compared to the urban development area, while remote suburban areas experience gradual deterioration as progression of urbanization. 3) The primary driving factors for ecological environment quality changes in WUDZ are increased urbanization and lake resource erosion. This study provides a quantitative method for the temporal monitoring of wetland urban EE, and provides a scientific basis for the rational formulation of policies and planning for the development of lake ecological space and the restriction of urban construction space.
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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 | 0.000 | 0.000 |
| 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 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".