Assessment of RAPTOR's linear programming approach in CAFASP3
Why this work is in the frame
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Bibliographic record
Abstract
We have developed a new algorithm based on the mathematical theory of linear programming (LP) and implemented it in our program RAPTOR. Our new approach provides an elegant formulation of the protein-threading problem, overcomes the intractability problem of protein threading, in practice, and allows us to use existing powerful linear programming software to obtain optimal protein threading solutions. CASP5 and CAFASP3 gave us the first chance to test RAPTOR in an unbiased way. RAPTOR was ranked as the top individual (automatic) server for fold recognition by the CAFASP3 organizers. In this short article, we describe RAPTOR's LP formulation, assess RAPTOR's performance in CAFASP3/CASP5, explain why it has superceded other existing automatic individual methods, and point out its strengths, limitations, extensions, and prospects for improvement.
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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 | 0.000 | 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