Level 2: Semi-Quantitative Seismic Risk Screening Tool (SQST) for existing wood light frame buildings under part 9 of the NBC. Part 1: user’s guide
Bibliographic record
Abstract
The Level 2 – Semi-Quantitative Seismic Risk Screening Tool (SQST) for existing wood light frame buildings under Part 9 of the NBC (WLF-P9) aims to inexpensively identify and exempt existing WLF-P9 buildings with acceptable seismic risks from Level 3 – Seismic Evaluation Guidelines (SEG) and to prioritize existing WLF-P9 buildings with potentially unacceptable seismic risks for Level 3 – SEG. It is preceded by the Level 1 – Preliminary Seismic Risk Screening Tool (PST) for existing WLF-P9 buildings under Part 9 of the NBC. The methodology in Level 2 – SQST (WLF-P9) is based on Level 2- SQST originally developed for existing buildings under Part 4 of the NBC (also known as Part 4 buildings). It consists of a structural scoring system, a non-structural component scoring system, and a ranking procedure. Level 2 – SQST (WLF-P9) is designed to be completed by trained screeners using Level 2 – SQST (WLF-P9) screening forms. A site visit is required for collecting building information such as building deterioration or damage and non-structural hazards.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.181 | 0.103 |
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".