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 classic preretirement problem for the financial planner is to advise a client how much to save, how much must be saved each year to reach a specified goal, and how the investments should be allocated between fixed income and equity. The traditional solution is to assume a fixed rate of return for each asset class and test scenarios until the mixture of variables yields a solution that meets the stated savings goal and seems feasible for the client. This binary result (accept or reject the plan) ignores the inherent uncertainty. In this paper, we derive a stochastic model in which the rate of return and the rate of increase of annual savings are both variable and calculate the probability that a particular goal will be achieved, given any initial savings endowment, periodic additional savings amount, mixture of assets (represented by the return distribution), and time to the goal. The calculations can be done on an Excel spreadsheet. We illustrate the use and numerical results of the model with a realistic retirement planning scenario and variations on it. While this solution is particularly important for preretirement planning, it applies generally to meeting any financial goal, such as saving a down payment for a house.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.031 |
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