Busting out of crystallography's Sisyphean prison: from pencil and paper to structure solving at the press of a button: past, present and future of crystallographic software development, maintenance and distribution
Bibliographic record
Abstract
The history of crystallographic computing and use of crystallographic software is one which traces the escape from the drudgery of manual human calculations to a world where the user delegates most of the travail to electronic computers. In practice, this involves practising crystallographers communicating their thoughts to the crystallographic program authors, in the hope that new procedures will be implemented within their software. Against this background, the development of small-molecule single-crystal and powder diffraction software is traced. Starting with the analogue machines and the use of Hollerith tabulators of the late 1930's, it is shown that computing developments have been science led, with new technologies being harnessed to solve pressing crystallographic problems. The development of software is also traced, with a final caution that few of the computations now performed daily are really understood by the program users. Unless a sufficient body of people continues to dismantle and re-build programs, the knowledge encoded in the old programs will become as inaccessible as the knowledge of how to build the Great Pyramid at Giza.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.093 | 0.041 |
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".