Fire plays a devastating role : World Trade Center building performance study
Bibliographic record
Abstract
The 9/11 terrorist incidents caused colossal destruction and significant damage to a number of buildings in the World Trade Center (WTC) vicinity of New York City (NYC), N.Y. It was the worst building disaster in history resulting in the largest loss of life from building collapse in North America. Following the disaster, the Federal Emergency Management Agency (FEMA), the Structural Engineering Institute of the American Society of Civil Engineers (SEI/ASCE), NYC and several other federal agencies and organizations established a team of experts to investigate the collapse and damage to the buildings. The building performance study (BPS) was led by FEMA, SEI and ASCE. The BPS team consisted of experts in tall buildings, steel structures, connections, fire engineering, blast effects and structural investigations.1The BPS team visited Ground Zero, surveyed the site, landfill and steel recycling centres, reviewed videotape records, eyewitness accounts, conducted interviews with building design teams and performed analyses using computer models. Based on this information, the BPS team compiled a report that was presented to the Science Committee of the U.S. Congress in May 2002.2 A brief overview of the factors leading to the collapse of the Twin Towers, the extent of damage and some of the key recommendations from the BPS, are presented here.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".