Urban sustainability in the context of global change : towards promoting healthy and green cities
Bibliographic record
Abstract
Global environmental change - the Singapore response, Victor R. Savage et al social sustainability in the Western Australian urban system - state planning strategies to 2029, Roy Jones sources of air pollution emissions in selected metropolitan cities of India, R.B. Singh and Bhuwan Kumar managing urban environmental pollution, Henk Vooged solid waste management -what's the Malaysian position?, Mohd. Nasir Hassan et al network management and the planning of mainports in the Netherlands, Peter P.J. Driessen and Pieter Glasbergen environmental and spatial aspect of urban development in the Lodz agglomeration, Tadeusz Marszat dynamics of urban land use in terms of land prices -comparative analysis of major cities in USA, Europe and Japan -Chicago, London, Dusseldorf, Sapporo and Osaka, Kiyotaka Jitsu city size distributions and metropolization, Denise Pumain and Francis Moriconi-Ebrard evaluating quality of urban environment using GIS, Wanglin Yan about a synthetic model - in Indian urbanization (the urban corridors), B.K. Roy ecological impact of the land use change in Delhi Ridge - anthropogenic stress and spatial realities, R.B. Singh and Rakhi Larijat urban geography for the 21st century, Denise Pumain urban landscape management plan and town planning - Japanese case, Kiyoko Kanki et al modelling city expansion based on remotely sensed images, Lin Li et al assessing the long-term hydrologic impact of urban sprawl -a practical geographic information system (GIS) based approach, Shilpam Pandey et al on migration, immigration and social sustainability - the recent Canadian experience, Larry S. Bourne.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".