A modified vapor-diffusion crystallization protocol that uses a common dehydrating agent
Bibliographic record
Abstract
In the vapor-diffusion protein-crystallization method, a small drop containing protein sample mixed with a crystallization solution is equilibrated against a reservoir solution in a sealed chamber. Whereas the chemical composition of the crystallization solution is critical for success, the primary role of the reservoir solution is to slowly concentrate the crystallization drop in a controlled fashion. Accordingly, it might be possible to use any reservoir solution of appropriate dehydrating strength. The important practical consequence is that many different experiments can share the same reservoir solution. This approach, called the ;shared reservoir solution' method, significantly simplifies manual and robotic experiment setup, reduces cost and allows a completely new design of optically superior and higher density crystallization plates. Although this research was motivated by these practical advantages, recent reports and the authors' results indicate that this method may actually increase crystallization success. The authors suggest that this may indicate that a protein has a preferred water activity for crystallization. Here, present practical and theoretical considerations as well as experimental tests of the shared reservoir solution method are presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".