Urban greening for low carbon cities—introduction to the special issue
Bibliographic record
Abstract
As a measure to counteract the effects of urban sprawl, with the continued growth of cities worldwide, different modes of urban greening are being increasingly recognized. This special issue addresses current developments in the transition to low carbon cities employing a variety of urban greening techniques. The special issue consists of 10 papers, including four review papers on the topics of biophilic architecture; environmental versus marketable aesthetics; urban agriculture; and the rationale for mainstreaming. It also contains several original research articles, some (about half of the special issue) presenting case studies, as for green redevelopment in Trenton, USA; facade greening in Genoa, Italy; climatic effects (on air temperature) in Rosario, Argentina; a modeling study for Melbourne, Australia; and another Australian case study on the greening and “un”greening of Adelaide. In addition to a broadly scoped paper that examines American stormwater management, the special issue also contains an editorial on technologies for wastewater treatment. Together, these papers constitute a contribution to recognize the importance of retaining greenery in cities chiefly, although not solely, as a countermeasure to urban sprawl and its environmental impacts. Urban greening here represents a cost-effective (soft) approach that is an effective tool as part of sustainable development.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".