DESIGNING URBAN OPEN SPACES OF BARDDHAMAN TOWN, WEST BENGAL, INDIA
Bibliographic record
Abstract
Located in 23 0 14'N latitude and 87 0 51’ E longitudes, Barddhaman, the Class I town (since 1961) is the head-quarter of the Barddhaman district, West Bengal, India. In term of size of population, the town ranked 13 th in the state. Being site and situational advantages, this agriculturally prosperous town since its inception has been recognised as the seat of settled civilization. Urban open spaces are important component to sustainable living in urban areas as they provide environmental, social and economic benefits. Barddhaman town has 122 open space sites comprising an area of 193 ha (8.59%): some are used as playground and rest are vacant as non-used assets. The present work intends to build a framework for rational use of open spaces for the sustenance of the town. For the present study 20 of such open spaces have been selected and comparatively analysed on the basis of standard score of two variables: distance from CBD (1) and areal extent (2) to find out their economic and environmental significance. Finally, a proposal has made for the uses of open spaces to make the town sustainable. Key words : Open space, town, CBD, standard score, environment, sustainable town
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".