Automatic local resolution-based sharpening of cryo-EM maps
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Recent technological advances and computational developments, have allowed the reconstruction of cryo-EM maps at near-atomic resolution structures. Cryo-EM maps benefit significantly of a “postprocessing” step, normally referred to as “sharpening”, that tends to increase signal at medium/high resolution. Here, we propose a new method for local sharpening of volumes generated by cryo-EM. The algorithm (LocalDeblur) is based on a local resolution-guided Wiener restoration approach, does not need any prior atomic model and it avoids artificial structure 1 factor corrections. LocalDeblur is fully automatic and parameter free. We show that the new method significantly and quantitatively improving map quality and interpretability, especially in cases of broad local resolution changes (as is often the case of membrane proteins).
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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 | 0.001 | 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