Bibliographic record
Abstract
Electrophoretic separation was first demonstrated in the year of 1807 and has since been a staple tool used by biologists and chemists for more than a century since its inception.From the initial crude paper electrophoresis system to today's modern automated electrophoresis system, the development of electrophoresis systems have been driven by the advancement of technology such as miniaturization, precision engineering, biochemistry, electrical and electronics.These advancements were introduced to meet the requirement for faster and better resolution of results.This paper reviews the evolution of the electrophoresis technology over one century and provides an insight into the possible future development of electrophoresis.Various aspects of the electrophoresis system such as the performances, designs, usages, separation phases, and biochecmistry were analysed.The technological advancements for this field have been evidenced by the increasing complexity of the electrophoresis system.A peek into the possible future for the world of electrophoresis has been provided by drawing insights from the missing links of current technologies.It is both exciting and equally perplexing to explore the promises that this seeming simple separation technology holds for the future.
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.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.943 | 0.946 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".