Cost Benefit Analysis of a Fire Safety System Based on the Life Quality Index
Bibliographic record
Abstract
Carrying out a cost benefit analysis requires, on the one hand, estimation of costs for the installation, running and maintenance of the system under consideration. On the other hand, it also requires estimation of the net reduction (in dollars) in property damage, as well as the effect on occupant injuries and fatalities. Costing of injuries does not raise ethical problems, but there is no universally accepted answer to the question "What is the value of human life?" Beever and Britton carried out a cost benefit analysis of various fire safety measures in one and two family dwellings in Australia but carried out analysis of the financial aspects separately from consideration of life safety. Thus it was not possible to uniquely rank the various options considered. In this paper their analysis is updated by integrating the financial aspects with the life safety aspects using a new approach, called the Life Quality Index (LQI) method, that has been developed by the Institute for Risk Research of the University of Waterloo, Canada. The life quality index can be calculated for many countries from widely available and reliable statistical data. It has been successfully used in environmental science and nuclear and structural engineering. When applied, as an example of the use of the method, to a cost benefit analysis of the use of sprinklers in one and two family dwellings in Australia using the Beever and Britton data, the LQI methodology yields a financial measure of the benefit expected if sprinklers were installed in one and two family dwellings. The analysis shows a very low benefit to cost ratio and it is thus concluded that installation of sprinklers in these dwellings is not cost effective
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".