Greenery as a factor shaping urban planning: the case of selected areas in Montreal
Bibliographic record
Abstract
In today’s dynamic urban context, where cities play a crucial role in shaping the quality of life for residents, the role of greenery as a determinant of public space has become a significant issue. This article focuses on analyzing the impact of greenery on shaping the urban fabric using Montreal as a case study, with particular emphasis on Mount Royal as a key element influencing spatial planning and city development. The author explores various aspects of this issue, including historical and cultural contexts, as well as practical implications. The genesis and development of the city are presented, along with the role that Mount Royal has played in the urbanization process. The analysis also encompasses urban planning strategies that focus on preserving and enhancing green spaces, such as parks and recreational areas, as well as sustainable city development. By delving into the various aspects of greenery presence in Montreal, including urban planning, park distribution, and social initiatives related to green spaces, this article aims to understand the complexity of the relationship between greenery and the shaping of public spaces in the context of this Canadian city. The analysis sheds light on existing challenges related to maintaining and developing green areas, while also highlighting the benefits of effectively utilizing greenery as a key element of urban planning.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".