Comparing scenarios to improve accessibility to local opportunities in Montreal, Canada.
Bibliographic record
Abstract
Density and diversity of land use are recognized as key features of neighbourhoods that can reduce car dependency and improve accessibility to local opportunities.This paper proposes to evaluate scenarios optimizing the spatial distribution of local opportunities to better align the distribution of both day population (where people conduct their activities) and night population (where people live).Hence, COVID-19 has changed the typical distribution of people across space, because of full or partial virtualization of activities, raising questions about the optimal distribution of opportunities.We thus evaluate the opportunity density per square kilometer for night and day population, with the current distribution of opportunities as well as two scenarios that distribute opportunities proportionally to the night and day populations.Furthermore, we evaluate two scenarios of restaurant redistribution that may result from the teleworking.The results indicate that the current distribution of opportunities is better aligned with the day population distribution.However, the population distribution could, in the upcoming years, shift towards the night population distribution with the increase in activity virtualization.Our findings show that strategic planning oriented towards a distribution in proportion to the night population would increase proximity for large proportion of the population in Montreal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".