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
Many important and practical problems can be expressed as optimization problems. Such problems involve finding the best of an exponentially large set of solutions. It can be like finding a needle in a haystack. The obvious algorithm, considering each of the solutions, takes too much time because there are so many solutions. Some of these problems can be solved in polynomial time using network flow, linear programming, greedy algorithms, or dynamic programming. When not, recursive backtracking can sometimes find an optimal solution for some instances in some practical applications. Approximately optimal solutions can sometimes be found more easily. Random algorithms, which flip coins, sometimes have better luck. However, for the most optimization problems, the best known algorithm require 2Θ(n) time on the worst case input instances. The commonly held belief is that there are no polynomial-time algorithms for them (though we may be wrong). NP-completeness helps to justify this belief by showing that some of these problems are universally hard amongst this class of problems. I now formally define this class of problems.
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 | 0.000 | 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 | 0.000 | 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