Modeling of Fire Contagion in Farms Insurance
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
In a farm, a fire that starts in any structure can spread to all other structures of that same farm, to barns, granaries, silos, etc.. Intuitively, we then expect that a farm with more structures will be more at risk of fire propagation than a smaller one. From an actuarial perspective, as the total premium for farm insurance is the sum of premiums of each structure of that farm, it is therefore necessary to propose a way to compute each premium by considering the risk of fire propagation. Based on the distances between structures on the same farm, we propose a new pricing approach that considers fire propagation. The proposed model makes it possible to analytically compute the probability of fire propagation as a function of the fire origin. This can in turn be used to price all individual structures of any given farm. A practical application of the model based on insurance data and satellite images is given. Address for Correspondence: boucher.jean-philippe@uqam.ca
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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