Micro Parallel Liquid Chromatography: Enabling Technology for Discovery Analytical Chemistry
Bibliographic record
Abstract
Since the introduction of combinatorial chemistry, compound libraries have undergone a significant increase in size and diversity. The ensuing expansion and diversification of compound libraries have resulted in increased demand for analytical throughput. Following the evolution of new technologies for generating lead compounds and targets and the desire to increase research and development productivity, analytical chemistry is now gaining attention as a bottleneck that would benefit from advances in instrumentation for increased analytical throughput. The commercial introduction of the Veloce trade mark micro parallel liquid chromatography system from Nanostream offers discovery analytical chemists the capability to analyze 24 samples in parallel with as little as 0.5 microl of sample. The system offers a scalable analytical approach to address bottlenecks in historically underserved areas, such as compound library purity screening, as well as higher value-added applications, such as log P determination and aqueous solubility assessment. This article describes the Veloce system and presents representative data from several discovery analytical applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".