Land Use Change and Urban Ecosystem Services: A Case Study of Urban Wetlands in a Rapidly Sprawling City in the Highlands of Chiapas, Mexico
Bibliographic record
Abstract
Urban wetlands provide a wide range of ecosystem services that are important for human-well-being. Despite their social and environmental importance, the degradation of urban wetlands continues mainly due to land use changes induced by rapid urbanization. Estimating the impact of these changes on ecosystem services is crucial to support the decision-making process of city planners at different levels. In this study, the spatial extents of the urban wetlands of San Cristobal de Las Casas, Chiapas, Mexico were determined for the years 2001 and 2018 in order to relate the spatial changes between these years with the provision and economic value of ecosystem services. Google Earth and SPOT imagery were used to evaluate land use/land cover changes while international coefficients were used to assess the value of the ecosystem services by category. Findings reveal a 7.3% decrease in the urban wetland area and a 12.5% increase of urban areas during the study period. The ecosystem service valuation shows that the total value flow decreased around $5 million (2007 USD) during the 17-year period, mainly due to decreases in the potential for regulating and cultural services. The use of freely available land use/land cover data together with global ecosystem service estimates reduce the cost of ground data collection and provides quick and reliable information that could help decision makers with land use planning in the context of data-scarce regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".