Simulation Engine for Adaptive Telematics Data
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
This article introduces a simulation engine for adaptive telematics (SEAT), which flexibly generates insurance claims datasets from driver telematics information that matches the specific profile of a target market. Generating adaptive telematics data via SEAT is a two-stage process. In the first stage, SEAT uses predetermined distributions of traditional policy characteristics from a target market as inputs and replicates these policy characteristics based on their distributions. In the second stage, SEAT generates the remaining covariates and insurance claims accordingly, based on configurations of the traditional policy characteristics with possible perturbations. We illustrate how SEAT generates an adaptive telematics dataset to match the South Korean insurance market and compare its behavior with the source telematics dataset on which the algorithm is based. We hope that both practitioners and researchers will use this publicly available simulation engine (https://github.com/bheeso/SEAT.git) and adaptive datasets to explore the usefulness of driver telematics data for developing diverse models of usage-based insurance. Address for Correspondence: himchan_jeong@sfu.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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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