Bibliographic record
Abstract
The latest merger of the neighbouring municipalities that currently form the City of Toronto occurred with the amalgamation of 1998.This new reality has many interesting situations that need addressing when considering a flexible and integrated bicycle network designed for the majority of residents and visitors.The portions of the city that follow a more suburban model do have the space for easy bicycle storage in the stock of single-family homes, yet the streets and outlying urban amenities aren't attached to a safe and convenient bicycling network.The mid and high rise towers that are aging in the suburbs and inner suburbs contend with the same lack of connections with bicycle lanes but have less storage options than the single family homes, and there are challenges to promoting safe biking and walking to schools.The inner city, and downtown revitalized areas that have been swept up with extensive condominium developments also suffer from a patchwork of bicycling street networks as well as limited parking options for those interested in cycling.Some of these challenges and opportunities of bicycling in the city of Toronto will be explored and presented as Toronto moves towards a city that accommodates all transportation choices, be they human-powered or otherwise.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".