Bibliographic record
Abstract
Canada is an immense archipelago of islands and satellite cities, with several highly urbanized regions around the great metropolises of Montréal, Toronto, Vancouver and Ottawa, where half of the population is concentrated. For the great metropolises with extreme climates, the underground could be an important resource that could modify the overall fruition of the urban structure, with an interesting impact on the organization of the territory and landscape.\nOne of the major cities built underground at the international level is the Ville\nSouterraine or RESO in Montréal, 32 km of pedestrian paths and shopping malls,\n12 square metres of multipurpose spaces perfectly integrated into 41 districts\nabove ground. Built in the 1960s and later expanded, this complex, which has 120 entrances, is used by 500,000 people every day.\nToronto, divided into two parallel cities, one on the surface and one underground, is internationally recognized for the PATH, one of the most extensive shopping complexes that is perfectly integrated with a pedestrian circulation system with facilities and vertical connections. The PATH plays an important role not only in the economic growth and urban evolution of Toronto, but is also an alternative way of experiencing the heart of the city. The Masterplan which was developed offers a targeted approach to growth and to the\ndevelopment of the underground city.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.044 |
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".