Integrating direct methods into a semi-automated, protein-phasing package
Bibliographic record
Abstract
Physical determination of the reflection phases is a fundamental problem in Xray crystallography.It is well know that the n-beam diffraction (n-BD) phenomenon can provide a direct solution for the 'phase problem', however, the phases extracted by the actual n-BD phasing techniques are not very precise, mainly due to the uncertainties on the kinematical diffraction present in the process.In this work, we present a simple and innovating mathematical expression -based on the second-order Born approximation-for 3-BD profile simulation, which is capable of dealing with a mixed dynamical/kinematical diffraction regime.By using the linear polarization of synchrotron radiation, a new procedure of X-ray data collection is employed to generate a polarization dependent dataset of 3-BD profiles.The fit of the dataset with the mathematical expression above mentioned has allowed triple-phase measurements with a precision better than 2˚ as the amount of kinematical diffraction is also determined.Examples are given for several single crystals.The effects of the incident beam divergences and energy resolution on the precision of this phasing technique is discussed as well as its efficiency for practical applications.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".