Bibliographic record
Abstract
Conventional microchip applications involving capillary electrophoresis (CE) typically inject a sample along one channel and use an intersection of two channels to define the sample plug--the portion of sample to be analysed along a second channel. In contrast to this method of zone separation, frontal analysis proceeds by injecting sample continuously into a single channel or column. Frontal analysis is more common in macroscopic procedures but there are benefits in sensitivity and device density to its application to electrophoresis on microchips. This work compares conventional microchip zone analysis with frontal analysis in the separation of PCR products. Although we detect on the order of 5000 fluorophores with a compact instrument using the zone separation CE method, we found a several-fold increase in the effective signal-to-noise ratio by using a frontal analysis method. By removing the need for additional channels and reservoirs the frontal method would allow device densities to be significantly increased, potentially improving the cost-effectiveness of microchip analyses in applications such as medical diagnostics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".