Quantification of green-blue ratios, impervious surface area and pace of urbanisation for sustainable management of urban lake – land zones in India -a case study from Bengaluru city
Bibliographic record
Abstract
Quantification of the ecosystem services of blue and green infrastructure in urban centres with the perspective of planning sustainable development is usually data-intensive, includes use of multi-platform datasets and adds to the complexities in deriving effective and reproducible metrics. The present study describes the creation of four simple metrics to estimate: 1. the ratio of ‘green’ vegetated areas to the ‘blue’ water spread areas, defined as the ‘Green-blue ratio’ (GBA); 2. The ratio of ‘blue’ water spread areas to ‘built-up’ ratio around the lakes, defined as the ‘Blue to Built-up ratio’ (BBA), 3. the percentage of impervious surface area (ISA) and 4. the pace of urbanisation in the dynamic zones (DZ) of urban lake environments. These new metrics were evaluated using landcover areas mapped from satellite imageries. Visual interpretation-based method was adopted to delineate the green, blue and built-up areas from Google Earth, which is suitable for wide range of users. The use of these metrics has been illustrated using available datasets for four representative lakes in Bengaluru city, India: Sankey tank, Ulsoor lake, Nagavarakere and Puttenahallikere. Significant spatio-temporal variations in the ratios of GBA and BBA as well as %ISA were observed and satisfactorily reflected the ecological status of these lakes in concurrence with earlier studies. Detailed analyses constrained a permissible rate of annual increase in the built-up area within the DZs to ∼ 3% for sustainable development of the lakes. The present set of metrics can be recommended as useful tools for urban planners and citizen scientists for seasonal monitoring of urban lake environments.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".