Fixed target combined with spectral mapping: approaching 100% hit rates for serial crystallography
Bibliographic record
Abstract
The advent of ultrafast highly brilliant coherent X-ray free-electron laser sources has driven the development of novel structure-determination approaches for proteins, and promises visualization of protein dynamics on sub-picosecond timescales with full atomic resolution. Significant efforts are being applied to the development of sample-delivery systems that allow these unique sources to be most efficiently exploited for high-throughput serial femtosecond crystallography. Here, the next iteration of a fixed-target crystallography chip designed for rapid and reliable delivery of up to 11 259 protein crystals with high spatial precision is presented. An experimental scheme for predetermining the positions of crystals in the chip by means of in situ spectroscopy using a fiducial system for rapid, precise alignment and registration of the crystal positions is presented. This delivers unprecedented performance in serial crystallography experiments at room temperature under atmospheric pressure, giving a raw hit rate approaching 100% with an effective indexing rate of approximately 50%, increasing the efficiency of beam usage and allowing the method to be applied to systems where the number of crystals is limited.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".