Development of an index assignment procedure compatible with the regional seismicity in the province of Quebec for the rapid visual screening of existing buildings
Bibliographic record
Abstract
In the context of the seismic vulnerability evaluation of buildings, the score assignment method can be used as the first step of a multiphase procedure aimed at identifying hazardous buildings that must then be analysed in greater detail. Because the existing Canadian rapid visual screening procedure has not been updated since 1992, a new procedure is proposed based on a set of vulnerability indices for different cities in the province of Quebec. A seismicity level (low, moderate, or high) is attributed to each city using the spectral acceleration response values included in the 2005 edition of National building code of Canada (NBCC) and the criteria proposed in FEMA 310 Handbook for the seismic evaluation of buildings - a prestandard. The structural vulnerability indices (SVIs) are calculated using the recently improved nonlinear static analysis procedure in FEMA 440, Improvement of nonlinear static seismic analysis procedures for each seismicity level. The NBCC 2005 reference soil class C is considered in the calculation of the SVIs, and index modifiers for the building height, irregularities, and design and construction year. The application of these indices to the estimation of the probable damage distribution in building inventories is discussed at the end.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".