When less is more: a more efficient vapour-diffusion protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it