EvoBot: An Open-Source, Modular, Liquid Handling Robot for Scientific Experiments
Bibliographic record
Abstract
Commercial liquid handling robots are rarely appropriate when tasks change often, which is the case in the early stages of biochemical research. In order to address it, we have developed EvoBot, a liquid handling robot, which is open-source and employs a modular design. The combination of an open-source and a modular design is particularly powerful because functionality is divided into modules with simple, well-defined interfaces, hence customisation of modules is possible without detailed knowledge of the entire system. Furthermore, the modular design allows end-users to only produce and assemble the modules that are relevant for their specific application. Hence, time and money are not wasted on functionality that is not needed. Finally, modules can easily be reused. In this paper, we describe the EvoBot modular design and through scientific experiments such as basic liquid handling, nurturing of microbial fuel cells, and droplet chemotaxis experiments document how functionality is increased one module at a time with a significant amount of reuse. In addition to providing wet-labs with an extendible, open-source liquid handling robot, we also think that modularity is a key concept that is likely to be useful in other robots developed for scientific purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".