Designing buildings to deliver city densification over transport infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>New York City constitutes one of the most extreme cases of urban concentration due to land scarcity. The development of skyscrapers has been one of the solutions to address the problem and, more often than not, new structures are being built directly over the extensive network of underground infrastructure in the city.</p><p>The presentation showcases the experience and lessons learned from a selection of projects in the US and UK located directly on top of existing infrastructure. Examples include NYC transit projects spanning from the 1990s, when nine buildings along Riverside Boulevard and on top of the former Penn Central rail yards were developed, to the ongoing Atlantic Yards/Pacific Park project where up to six buildings will be erected on top a 320,000sqft platform over train tracks. Amongst other projects, the presentation will include the Hudson Yards development on the east side of Midtown Manhattan located over the subway entrance to the 34th Street station and the Waterline project built over the AmTrak and LIRR train tracks. In the UK case studies include two recent London projects at Royal Mint Gardens and Principal Place. The general focus is aimed at the structural strategies employed and their impact not only in construction costs but also the long-term effect in the urban fabric.</p>
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it