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
Abstract Risk theory studies insurance risk models that describe the uncertainty associated with the claims recorded by an insurance company for the losses incurred by its policy holders. From premium and investment income, insurers set aside funds (surplus) to cover such losses. Ruin theory studies the fluctuations of these surplus processes. Classical problems include ruin (low) and dividend (high) barrier hitting times. In the last decade, the expected discounted penalty function, proposed by Gerber and Shiu 5, has unified the treatment of the joint distribution of the time to ruin, the surplus just prior to ruin, and the deficit at ruin. This article centers on this expected discounted penalty function, commonly called the Gerber–Shiu (G–S) function in the actuarial literature. The G–S function is somewhat akin to an expected discounted payoff function for financial instruments. A brief description of its general features is given here, together with references that discuss details, generalizations, and applications to insurance and finance.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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