Bibliographic record
Abstract
After 25 years from its first formulation, Transit Oriented Development (T.O.D.) is going to be applied in several metropolis of east and middle east, where new metro lines are under implementation; for some of those the public transportation is arriving for the first time, or is going to be massively implemented, as in Saudi Arabia.Beside developing infrastructures for public transportation, the new metro projects are going to bring a new improvement in the urban structure, from a mono-centric and autooriented, into a pedestrian-friendly and transit-oriented metropolis.The success of the new Metro will pass by the successful development of the involved districts as T.O.D. neighbourhoods.This is going to be successful only working by procedural strategies on strongly integrated methodologies for analysis, design, maintenance as post production and the management of the whole process, before than just going for the usual planning and urban or architectural design projects.Starting from the guidelines of Transit Oriented Development Institutes and the several case studies in the USA, the research will extend into the most successful urban development strategies in Europe, through direct experience and monitoring of the results for the selected projects within the last 15 years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".