A versatile and low-cost open source pipetting robot for automation of toxicological and ecotoxicological bioassays
Bibliographic record
Abstract
In the past decades, bioassays and whole-organism bioassay have become important tools not only in compliance testing of industrial chemicals and plant protection products, but also in the monitoring of environmental quality. With few exceptions, such test systems are discontinuous. They require exposure of the biological test material in small units, such as multiwell plates, during prolonged incubation periods, and do not allow online read-outs. It is mostly due to these shortcomings that applications in continuous monitoring of, e.g., drinking or surface water quality are largely missing. We propose the use of pipetting robots that can be used to automatically exchange samples in multiwell plates with fresh samples in a semi-static manner, as a potential solution to overcome these limitations. In this study, we developed a simple and low-cost, versatile pipetting robot constructed partly using open-source hardware that has a small footprint and can be used for online monitoring of water quality by means of an automated whole-organism bioassay. We tested its precision in automated 2-fold dilution series and used it for exposure of zebrafish embryos (Danio rerio)-a common model species in ecotoxicology-to cadmium chloride and permethrin. We found that, compared to conventional static or semi-static exposure scenarios, effects of the two chemicals in zebrafish embryos generally occurred at lower concentrations, and analytically verified that the increased frequency of media exchange resulted in a greater availability of the chemical. In combination with advanced detection systems this custom-made pipetting robot has the potential to become a valuable tool in future monitoring strategies for drinking and surface water.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".