Gold-standard local validation enhances interpretability of cryo-EM maps
Bibliographic record
Abstract
Accurate interpretation of cryo-electron microscopy (cryo-EM) maps requires robust local measures of quality and resolution. Current approaches are often limited by single-map estimates, user-defined solvent masks that risk bias and overfitting, and an exclusive reliance on signal amplitudes while neglecting phase information. Here, we present a Gold-standard framework requiring minimal user intervention for local validation and enhancement of cryo-EM reconstructions that addresses these limitations by jointly analyzing amplitudes and phases between independent half-maps or between maps and atomic models. All methods are computed voxel-wise in real space from independent half-maps, ensuring unbiased local evaluation. The toolbox includes unbiased local sharpening (LocSpiral2), per-voxel figure-of-merit, signal-to-noise estimation and general quality assessment (LocFOM, LocSNR and LocQ), directional local anisotropy evaluation (LocAnisotropy) and Gold-standard local resolution mapping (LocResMap). Together, these approaches provide a comprehensive and unified strategy for evaluating cryo-EM maps at the voxel level, improving interpretability and offering more reliable guidance for atomic modelling. Applications to challenging experimental datasets demonstrate enhanced map sharpening, robust local validation, and improved structural insights.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".