Canadian fire statistics tool technical document
Bibliographic record
Abstract
The analysis of fire statistics can provide a great deal of information that can be used to understand a fire problem, factors affecting life safety and reveal general trends and patterns. Therefore, the Fire Research Program (FR) at the Institute for Research in Construction took the initiative to gather Canadian fire incidents data. This data was consolidated into two databases from which the data can be easily accessed and updated as new data becomes available. In addition, FR has developed a tool that provides a user interface to the databases, allowing the user to easily view and update the data. The tool is also capable of generating various charts and tables, which enables the user to quickly create informative reports on the data when needed. This report describes the design and the functionality of the Canadian Fire Statistics tool.The development of the Canadian Fire Statistics tool has used Microsoft Access 2000 to create the databases and Microsoft Visual Basic 6 to create the user interface that interacts with the databases.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.080 |
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".