London Expanding - Adding Value Through Fine Engineering
Bibliographic record
Abstract
<p>London combines a rapidly expanding population with ever-decreasing land availability. This equation continues to attract property investors and allows developers to deliver high quality buildings.</p><p>Typically, developments must respect local site constraints. London’s rich construction archaeology – from Roman times to the post-war period – and the need to future-proof new infrastructure, create a unique blend of challenging constraints.</p><p>Unlocking such highly constrained sites by devising finely-engineered, sustainable and cost-efficient solutions has generated some of London’s most iconic buildings. A typical example is the recently completed Principal Tower, a 50-storey residential development on the edge of the City. Sited between existing 19th century railway tunnels and a protected viewing corridor that restricts building heights, the tower also sits above provision for a future rail tunnel.</p><p>WSP overcame these extreme constraints by forming a deep ‘concrete box’ through the building’s basement to support both the tower and the future railway tunnel. Adopting solutions associated more with heavy civil engineering adds significant costs, but enables high value developments on otherwise unremarkable sites.</p><p>This paper will examine some of London’s most technically challenging sites, such as Principal Tower, 22 Bishopsgate and Shard Place and the advanced engineering solutions that have made these iconic buildings possible. Further details in the design of 22 Bishopsgate are given in Paper No 16601: Twentytwo Bishopsgate, London.</p>
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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 | 0.000 | 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 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".