Level 2: Semi-Quantitative Seismic Risk Screening Tool (SQST) for Existing Buildings. Part 1: user's guide
Bibliographic record
Abstract
The Level 2 – SQST adopts a methodology based on exterior and interior visual screening of buildings using the Level 2 – SQST screening forms completed by trained screeners with basic knowledge in seismic design and assessment of buildings. The Level 2 – SQST screening forms provide space for documenting collected data. Based on collected building information in office and site visits, structural and non-structural component scores are calculated to estimate the expected seismic risk of buildings. Structural and non-structural component scores are compared with corresponding thresholds to determine whether the seismic risk posed by structural and non-structural components is acceptable. Given this, the building owner is expected to assume the level of risk associated with the structural and non-structural component scores. Buildings identified as potentially hazardous are flagged for seismic evaluation. The Level 2 – SQST also identifies special conditions that immediately trigger seismic evaluation. These conditions are (1) unknown model building type, (2) federal heritage designation, (3) change of occupancy results in increase of structural loads, (4) current consequences of failure higher than original consequences of failure, (5) Site Class F, (6) presence of geologic hazards, and (7) significant building deterioration or damage. The Level 2 – SQST can also be used for prioritizing buildings requiring seismic evaluation. Buildings that are spread over a large region or across the country can be ranked based on the structural and non-structural component prioritization indexes that are functions of structural and non-structural scores and consequences of failure of the buildings. Furthermore, the ranking can be used to develop building inventories for regional and national earthquake damage and loss assessment.
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.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".