Combinatorial Dispensing as a Fast and Efficient Means to Create Complex Screens
Bibliographic record
Abstract
Liquid handling robots carry out tasks from simple plate filling to complex operations such as creating reagent cocktails from multiple stock solutions. The latter task is conceptually a combinatorial process where each cocktail is created by combining a subset of stock solutions in user-defined volumes. General-purpose liquid handlers can perform this task, but their hardware lacks the inherent properties needed to exploit the combinatorial nature of the problem at hand. Here we present the use of non-contact dispensing technologies to create complex screens at low volume and high density. Our approach is based on the "inkjet printer principle" where a block of dispensers (print head) travels over a multi-well plate (paper) to deliver the reagents (inks) in a user-defined pattern. Impact-induced mixing and the lack of tip contamination remove the need for extensive tip washing or the use of large numbers of disposable tips. As an example, protein crystallization screening is used to demonstrate the technology. This application requires the creation of complex mixtures from many stock solutions with a great diversity of viscosities and surface tensions. In addition, dispense volumes cover a range from 50 nL to 50 microL, illustrating its utility in low-volume high-density screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".