<i>RADDOSE-XFEL</i>: femtosecond time-resolved dose estimates for macromolecular X-ray free-electron laser experiments
Bibliographic record
Abstract
For macromolecular structure determination at synchrotron sources, radiation damage remains a major limiting factor. Estimation of the absorbed dose (J kg−1) during data collection at these sources by programs such asRADDOSE-3Dhas allowed direct comparison of radiation damage between experiments carried out with different samples and beam parameters. This has enabled prediction of roughly when radiation damage will manifest so it can potentially be avoided. X-ray free-electron lasers (XFELs), which produce intense X-ray pulses only a few femtoseconds in duration, can be used to generate diffraction patterns before most of the radiation damage processes have occurred and hence hypothetically they enable the determination of damage-free atomic resolution structures. In spite of this, several experimental and theoretical studies have suggested that structures from XFELs are not always free of radiation damage. There are currently no freely available programs designed to calculate the dose absorbed during XFEL data collection. This article presents an extension toRADDOSE-3DcalledRADDOSE-XFEL, which calculates the time-resolved dose during XFEL experiments. It is anticipated thatRADDOSE-XFELcould be used to facilitate the study of radiation damage at XFELs and ultimately be used prior to data collection so that experimenters can plan their experiments to avoid radiation damage manifesting in their structures.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".