Characterizing Ecological Sensitivity of Yangtze River Delta Urban Agglomeration in 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
Ecological sensitivity, as one of the most important indicators to evaluate regional environmental issues, holds significant implications for ecological governance and management in the related area. This study utilized remote sensing imagery of Landsat Thematic Mapper (TM) from the Yangtze River Delta (YRD) in 2014 and 2018, combined with field surveys and socio-economic data. Considering the local ecological and environmental conditions in the region, nine factors related to seven aspects, soil erosion, topography, humidity, habitat, water environment, human interference, and climate, were selected to create an ecological sensitivity evaluation framework for the YRD urban agglomeration. The coefficient of variation method was applied to determine factor weights, while the zonal statistics and spatial overlay methods were used for a comprehensive analysis of ecological sensitivity in a geographic information system (GIS). The YRD urban agglomeration was categorized into five ecological sensitivity levels: extremely sensitive, highly sensitive, moderately sensitive, slightly sensitive, and insensitive. The analysis results revealed spatial variations in the distribution of ecological sensitivity across the YRD urban agglomeration, with the overall ecological sensitivity level being slightly sensitive. The proportions of the total area occupied by extremely sensitive, highly sensitive, moderately sensitive, slightly sensitive, and insensitive zones were 14.30%, 12.02%, 25.29%, 30.34%, and 18.05% in 2014, and 14.30%, 24.01%, 16.33%, 27.32%, and 18.05%, respectively, in 2018. Based on these results, relevant ecological vulnerabilities for the YRD urban agglomeration were discussed.
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.
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) | 0.000 | 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