A nanovolume crystallization robot that creates its crystallization screens on-the-fly
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
Protein crystallization generally consists of an initial screen followed by optimization of promising conditions. Whereas the initial screen typically uses a standard set of pre-made crystallization cocktails, optimization requires new cocktails with small perturbations of the original composition. Highly parallel synchronous crystallization robots are ideal for initial screening, but they depend on pre-made crystallization cocktails. Asynchronous crystallization robots can create crystallization cocktails from stock solutions, but in practice this ability is rarely exploited. Instead, large-scale operations typically use a general liquid-handling robot to create optimization screens, whereas academics mostly rely on manual optimization. Here, the use of an asynchronous crystallization robot to create customized crystallization cocktails and set up nanovolume crystallization experiments without a compromise in speed or drop quality is described. This approach avoids the complex integration of hardware, software and dataflow between two robots and saves cost and space. As a proof of principle, a commercial crystal screen has been reproduced with the robot and shows that results are virtually identical to using the actual commercial screen.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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