The environmental impact of urban land use and transportation strategies in the City of Toronto
Bibliographic record
Abstract
The objective of researchers, planners and engineers is to present to society a viable infrastructure that is sustainable as well as environmentally friendly. Since the growing population, as well as the socio-economic growth of an urban landscape, creates greater and greater travel demands, the effects that these increases cause on our environment and social fabric can not be underestimated. It may appear impossible to devise a plan that will provide a sustainable system, but the crux of the matter is to recognize the importance of this vision as a process. Today, thousands of researchers worldwide are working to develop solid plans and a timeline to implement the many good ideas that have been brought forward. The development of urban land increases demand for an extensive transportation infrastructure. The impact of land use on transportation, and vice-versa, eventually boils down to its impact on our environment. This project elaborates the inter-connected relationship of urban land use with transportation infrastructure and identifies the regions in the City of Toronto where land-use activities are not compatible with the transportation system. The analysis of this research is based on data from the Transportation Tomorrow Survey (TTS). This paper not only elaborates on these issues but also addresses the requisite improvements that could significantly enhance the quality of the environment for us all in a broader vision of a more sustainable society.
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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".