Decadal-Scale Vegetation Dynamics of Kolkata and Its Surrounding Areas, India Using Fuzzy Classification Technique
Bibliographic record
Abstract
Vegetation is an important component of any ecosystem. In urban areas, presence of vegetation is essential for reducing the effects of environmental pollution and maintaining the ecological balance. In the wake of excessive growth of population, the urban vegetation with parkland, especially in developing countries, are diminishing rapidly to provide additional space to various other types of land use. However, such reductions can have serious future implications. Therefore, an assessment of the vegetation cover of urban areas is essential. In this paper, Landsat satellite imageries have been used to study the changes in the vegetation cover of Kolkata, the largest metropolis in eastern India, from 1973 to 2011. The entire area of Kolkata and its surroundings (up to a distance of 10 km) has been divided into four quadrants according to the cardinal directions (northeast, southeast, southwest and northwest) and six concentric rings of 2 km radius each, and from these 24 sectors (four quadrants and six concentric circles), vegetated lands have been identified for analyzing the changes during the study period. There is a constant decrease of vegetation cover from 1973 till date. The outer periphery of the city is characterized by more urban vegetation as compared to the core of the city. Apart from this, the western sector is denser in terms of vegetation than the eastern one.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".