An evaluation of flood control and urban cooling ecosystem services delivered by urban green infrastructure
Bibliographic record
Abstract
To inform planning decisions and address climate change impacts in expanding cities, it is desirable to quantify urban ecosystem services like flood control and urban cooling. By comparing with a purpose-built habitat map, this study ground-truthed a method to assess flood control, which was developed by Southampton City Council from surface maps. It was confirmed that infiltration capacity is a good proxy for flood control, leaf area index could represent urban cooling, and thereby both could be used to score urban surface types. A two-tiered system was proposed so that surface maps would be used for city-wide scale, and as they produce similar results that are more accurate at fine scales, habitat maps are used at site level. These surrogates were integrated to produce a Green Space Factor for flood control and urban cooling, wherein a combined score can be generated for particular locations. This could be extended further to include other ecosystem services. The new integrated multi-scale ecosystem service quantification tool could be used by developers and policy-makers to identify target areas in their projects and policies that could benefit from enhanced green infrastructure.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".