Educational Reconnaissance of the Area Affected by the 1999 Chi‐Chi Earthquake—Three Years Later
Bibliographic record
Abstract
Findings from a reconnaissance effort to the area affected by the 1999 Chi‐Chi earthquake in Taiwan are reported in this paper. The reconnaissance team comprised eight graduate students from the three U.S. earthquake engineering research centers (MAE, MCEER, and PEER). The mission provided an opportunity to assess, three years later, the response of the engineering community to this major earthquake that caused extensive loss of life and property. This educational reconnaissance effort was hosted by Taiwan's National Center for Research in Earthquake Engineering (NCREE) in May 2002. Researchers from NCREE first presented extensive information on the observed failures, the repairs and reconstruction, as well as the lessons learned and changes in future engineering practice before an intensive site visit. In this paper, observations on the performance/repair/retrofit and reconstruction of residential buildings and bridges are reported. Although most bridge structures were retrofitted or rebuilt with state‐of‐the‐art engineered solutions, most low‐rise mixed commercial‐residential buildings were retrofitted with non‐engineered techniques often conceived and carried out by local contractors. Furthermore, the structural configuration of the mixed commercial‐residential building that suffered the most serious damage is still rather commonly applied in Taiwan. The organization and the educational value of this reconnaissance experience are also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".