IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories
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
Self-driving laboratories (SDLs), powered by robotics, automation and artificial intelligence, accelerate scientific discoveries through autonomous experimentation. However, their adoption and transferability are limited by the lack of standardized software across diverse SDLs. In this work, we introduce IvoryOS - an open-source orchestrator that automatically generates web interfaces for Python-based SDLs. It ensures interoperability by dynamically updating the user interfaces with the plugged components and their functionalities. The interfaces enable users to directly control SDLs and design workflows through a drag-and-drop user interface. Additionally, the workflow manager provides no-code configuration for iterative execution, supporting both human-in-the-loop and closed-loop experimentation. We demonstrate the integration of IvoryOS with six SDLs across two institutes, showcasing its adaptability and utility across platforms at various development stages. The plug-and-play and low-code feature of IvoryOS addresses the rapidly evolving demands of SDL development and significantly lowers the barrier to entry for building and managing SDLs.
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.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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