Optimizing<i>DREAR</i>and<i>SnB</i>parameters for determining Se-atom substructures
Bibliographic record
Abstract
The determination of the anomalous scattering substructure is the first essential step in any successful macromolecular structure determination using the multiwavelength anomalous diffraction (MAD) technique. The diffE method of calculating difference Es in conjunction with SnB has had considerable success in determining large Se-atom substructures. An investigation of the parameters used in both the data-reduction and error-analysis routines (DREAR) as well as the SnB phasing process itself was undertaken to optimize these parameters for more efficient use of the procedure. Two sets of selenomethionyl S-adenosylhomocysteine hydrolase MAD data were used as test data. The elimination of all erroneously large differences prior to phasing was found to be critical and the best results were obtained from accurate highly redundant intensity measurements. The high-resolution data collected in the typical MAD experiment are sufficient, but the inclusion of low-resolution data below 20 A improved the success rate considerably. Although the best results have been obtained from single-wavelength peak anomalous diffraction data alone, independent SnB analysis of data measured at other wavelengths can provide confirmation for questionable sites.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".