Conception through Build of an Automated Liquids Processing System for Compound Management in a Low-Humidity Environment
Bibliographic record
Abstract
Boehringer Ingelheim's Automated Liquids Processing System (ALPS) in Ridgefield, Connecticut, was built to accommodate all compound solution-based operations following dissolution in neat DMSO. Process analysis resulted in the design of two nearly identical conveyor-based subsystems, each capable of executing 1400 × 384-well plate or punch tube replicates per batch. Two parallel-positioned subsystems are capable of independent execution or alternatively executed as a unified system for more complex or higher throughput processes. Primary ALPS functions include creation of high-throughput screening plates, concentration-response plates, and reformatted master stock plates (e.g., 384-well plates from 96-well plates). Integrated operations included centrifugation, unsealing/piercing, broadcast diluent addition, barcode print/application, compound transfer/mix via disposable pipette tips, and plate sealing. ALPS key features included instrument pooling for increased capacity or fail-over situations, programming constructs to associate one source plate to an array of replicate plates, and stacked collation of completed plates. Due to the hygroscopic nature of DMSO, ALPS was designed to operate within a 10% relativity humidity environment. The activities described are the collaborative efforts that contributed to the specification, build, delivery, and acceptance testing between Boehringer Ingelheim Pharmaceuticals, Inc. and the automation integration vendor, Thermo Scientific Laboratory Automation (Burlington, ON, Canada).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".