FIRECAT: Fire Research Catalogue of Mass Timber Compartment Tests
Bibliographic record
Abstract
Various research groups have conducted numerous mass timber compartment fire tests to validate the safety of mass timber as a structural material. Each test often generates gigabytes of measurement data. Unfortunately, the in-depth temperature measurements have always been underutilized. The temperature-time-location profiles are not widely shared in a usable format; they are often available only as plots and summary variables, such as charring rates, in the published literature. Moreover, due to significant variations in the experimental designs used by different research groups, it is challenging to make meaningful comparisons between the thermal profiles developed within structural members in dissimilar compartment configurations across different experimental campaigns. These roadblocks have long discouraged meta-analyses and other attempts to collate and analyze solid temperature data across different campaigns. This gap is addressed by introducing the Fire Research Catalogue of Mass Timber Compartment Tests (FIRECAT). This public, unified, and searchable relational database catalogues the in-depth temperature profiles developed in structural timber. Besides the temperature-time-location data, the database expresses test metadata with over 110 physical variables at the compartment and member levels, which are believed to influence the gas dynamics and the thermal response of the timber within the compartment. The FIRECAT database and its interactive public dashboard will serve as a resource for researchers and practitioners to benchmark structural thermal models for mass timber, perform meta-analyses, and develop scientific machine learning models for fire design. The interactive dashboard based on the latest version of FIRECAT is available here. No datasets are added to FIRECAT without properly citing the publication from which they were extracted. All citations can be found under the “campaign_info” field in the database for each test. Alongside maintaining proper attribution, the extraction of datasets from other publications does not constitute a transfer of data ownership, and the original dataset creators can contact us at any time to request their data to be corrected or withdrawn. If you wish to share your feedback, or to contribute your data to FIRECAT, kindly fill out our User & Contributor Feedback Survey. Data contributors should use the input template provided below (new_data_input_template.xlsx) to format their data prior to uploading it through the survey platform. For all other inquiries, please contact Arwa Abougharib via email (see contact information in Readme.md).
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 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.035 |
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".