Characterization of Injected Sample Plugs in Microchip Capillary Electrophoresis
Bibliographic record
Abstract
Since the capillary electrophoresis was proposed to be run in the chip format, tremendous studies have been performed by covering many different aspects of this technology. One key element is the sample plug generated between the injection and separation process, because it will play a governing role on the final separation performance, i.e., the separation efficiency depends on the initial sample plug and its further dispersion development. In literature, some work has been done previously to generate various sample plugs, or optimize them by means of either channel design or operational control. However, little work has been reported to characterize the sample plug with evaluating parameters. Usually, the well-defined and reproducible sample plug is anticipated for high quality separation. By experience, thin-rectangular sample plugs are normally assumed, but not technically proved yet, to have superior performance in electrophoretic separation. Quantitative study is necessary to be performed to demonstrate the relevant qualitative estimation or analysis. All above stated are the motivation of current work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".