Squeezing the most from every crystal: the fine details of data collection
Bibliographic record
Abstract
Modern synchrotron beamlines offer instrumentation of unprecedented quality, which in turn encourages increasingly marginal experiments, and for these, as much as ever, the ultimate success of data collection depends on the experience, but especially the care, of the experimenter. A representative set of difficult cases has been encountered at the Structural Genomics Consortium, a worldwide structural genomics initiative of which the Oxford site currently deposits three novel human structures per month. Achieving this target relies heavily on frequent visits to the Diamond Light Source, and the variety of crystal systems still demand customized data collection, diligent checks and careful planning of each experiment. Here, an overview is presented of the techniques and procedures that have been refined over the years and that are considered synchrotron best practice.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".