Cumulative and relative cumulative residual information generating measures and associated properties
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 this work, we propose cumulative residual information generating (CRIG) and relative cumulative residual information generating (RCRIG) measures and then establish some of their properties. A new divergence measure based on the CRIG function is proposed to measure the closeness between two survival functions as well as a cumulative residual Kullback-Leibler divergence. We also present Jensen-cumulative residual information generating function, whose derivatives generate some new cumulative information measures such as Jensen-cumulative residual Taneja entropy, Jensen-fractional cumulative residual entropy and Jensen-Gini mean difference measure. We further show that the Jensen-cumulative residual information generating function can be expressed as a mixture of two versions of the proposed new divergence measure.
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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 | 0.003 | 0.022 |
| 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