Hybrid Fire Testing for Performance Evaluation of Structures in Fire - Part 2: Application
Bibliographic record
Abstract
A hybrid fire testing (HFT) approach was carried out by means of both computer simulation and experimentation using the National Research Council Canada's (NRC) testing facilities in Ottawa. Fire structural performance of a 3D full-scale 6-storey building structure was tested for a fire compartment scenario in the main floor of the building. The column in the designated fire compartment was exposed to the fire in a column furnace and the rest of the building was simulated using a numerical modeling. The methodology of the HFT and its numerical verifications were developed and described in a previous report. This report includes application of the HFT and its displacement results for fire structural performance of the whole 6-storey building. It also includes results of a separate column tested in fire using the traditional fire resistance standard test method. The second column specimen was identical to that of the column tested using the HFT. A comparison is provided between the results of the standard test and the HFT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".