When less is more: a more efficient vapour-diffusion protocol
Bibliographic record
Abstract
Reducing protein consumption during crystallization screening is of utmost importance to crystallographers because of the time, effort and money that goes into producing pure protein. One approach is to reduce sample volumes with robotics, but a patent and the high cost of equipment limits access. Here, it is shown that the same result can be obtained by reducing the sample concentration in a modified vapour-diffusion protocol, the dilution method. In this protocol, the protein and mother liquor in the crystallization drop are both diluted, while the mother liquor in the well remains undiluted. Vapour diffusion will shrink the initial volume of the crystallization drop, e.g. 1 micro l or more, to a drop size equivalent to one dispensed by a robot. This new crystallization method circumvents some of the current problems associated with robotic crystallization screening trials. Because of the large initial volume of the crystallization drop, the evaporation problem is eliminated and dispensing accuracy is improved. In addition, the likelihood that the crystallization experiment starts in the undersaturated region is increased.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".