Why this work is in the frame
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Bibliographic record
Abstract
Motivated by applications in compression and distributed transform coding, we propose a new gossip algorithm called selective gossip to efficiently compute sparse approximations of network data. We consider running parallel gossip algorithms on the elements of a vector of transform coefficients. Unlike classical randomized gossip, communication between adjacent nodes is data driven and only performed if deemed to significantly improve the estimate of the signal vector. In particular nodes adaptively estimate and focus on using communication resources to compute significant coefficients (above a pre-defined threshold in magnitude). Consequently, energy and bandwidth are conserved by not gossiping on insignificant coefficients. The proposed procedure guarantees that all nodes will reach consensus on (i) the values of significant coefficients and (ii) the indices of insignificant coefficients. Insignificant values are not computed. We illustrate the significant communication savings over global randomized gossiping in a distributed transform coding application.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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