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
We propose a new method for estimating an unknown regression function $f_*:[\alpha, \beta] \rightarrow \mathbb{R}$ from a dataset $(X_1, Y_1), \dots, (X_n,$ $Y_n)$ when the only information available on $f_*$ is the fact that $f_*$ is convex and twice differentiable. In the proposed method, we fit a convex function to the dataset that minimizes the sum of the roughness of the fitted function and the average squared differences between the fitted function and $f_*$. We prove that the proposed estimator can be computed by solving a convex quadratic programming problem with linear constraints. Numerical results illustrate the superior performance of the proposed estimator compared to existing methods when i) $f_*$ is the price of a stock option as a function of the strike price and ii) $f_*$ is the steady-state mean waiting time of a customer in a single server queue.
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 | 0.010 |
| 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