A remote sensing-based strategy for mapping anthropogenic urban surface ecological poorness zones (AUSEPZ): A case study of Lisbon City
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
Anthropogenic activities play a crucial role in the formation and intensification of Urban Surface Ecological Poorness Zones (USEPZ). This study introduces a methodology for assessing the spatiotemporal fluctuations of Anthropogenic USEPZ (AUSEPZ), using Lisbon city and the Setúbal district as a case study to demonstrate its effectiveness. By integrating data from various surface characteristics through the Comprehensive Ecological Evaluation Index (CEEI), Surface Ecological Condition (SEC) maps were developed, and their spatial and temporal variations were analyzed. Additionally, a feature space was established between the Impervious Surface Percentage (ISP) and CEEI to calculate AUSEPZ intensity across different years. The findings revealed that the mean CEEI of Lisbon increased by 0.41 between 1986 and 2023. During this period, the proportions of SEC classified as Excellent, Very Good, Good, Fair, and Poor changed by −52 %, −13 %, +107 %, +444 %, and + 1134 %, respectively. The AUSEPZ intensity values for Lisbon were 0.32, 0.39, 0.46, 0.52, 0.57, and 0.63 for the years 1986, 1994, 2001, 2008, 2015, and 2023, respectively. The intensification of human activities, driven by urban expansion and population growth, has significantly contributed to the deterioration of SEC in Lisbon over recent years. These findings provide valuable insights for urban planners, policymakers, and stakeholders, enabling the design of targeted strategies to mitigate the impacts of urbanization and enhance ecological conditions in urban areas.
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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) | 0.001 | 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 it