Household Level FireSmart Adaptation Cost Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effects of global climate change are increasing the frequency and intensity of wildfires in North America. The continued growth of wildland-urban interface (WUI) communities are placing more and more homes and businesses in regions where wildfires are a common occurrence. Without incentives and cost offsets from private insurers and government, homeowners have little incentive to invest in FireSmart adaptations to their property. In densely built neighbourhoods, a classic free rider problem develops where neighbours benefit from the FireSmart adaptations of their neighbours, but, in turn, place their neighbours at risk by remaining susceptible to fire. A cost analysis of FireSmart’s homeowner recommendations was conducted to estimate the compliance costs faced by the average homeowner in Fort McMurray, Alberta. This study determined that, over the lifecycle of a home, FireSmart’s recommended adaptations cost approximately 4% of average property value. If levels of government were to include fire-resistant adaptations within current home renovation rebate programs and if insurers were to include wildfire risk in their actuarial calculations, homeowners would benefit from increased awareness and financial incentives to carry out fire resistant adaptations on their property. Discipline: Economics Faculty Mentor: Dr. Rafat Alam
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it