Mapping Green Infrastructure Based on Multifunctional Ecosystem Services: A Sustainable Planning Framework for Utah’s Wasatch Front
Bibliographic record
Abstract
Most sustainable planning frameworks assess natural and social–economic landscape systems as separate entities, and our understanding of the interrelationships between them is incomplete. Landscape classification in urbanizing environments requires an integrated spatial planning approach to better address the United Nation’s sustainable development challenges. The objective of this research is to apply a multicriteria evaluation which ranked diverse ecosystem–service producing landscapes and synthesize the findings within a unique green infrastructure spatial planning framework. Local government stakeholder derived weighting and GIS classification were operated to map both the urban and natural landscapes of the Salt Lake City region of Utah, one of the most rapidly urbanizing areas in North America. Results were assimilated through five regional landscape typologies—Ecological, Hydrological, Recreational, Working Lands, and Community—and indicated those highest ranked landscape areas which provided multiple ecosystem services. These findings support collaborative decision making among diverse stakeholders with overlapping objectives and illustrates pathways to the development of ecosystem service criteria. This paper contributes to a better understanding of how to integrate data and visualize the strategic approaches required for sustainable planning and management, particularly in urban and urbanizing regions where complex socioecological landscapes predominate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".