Flow-through analytical systems and microsystems with electrochemical detection for monitoring of biologically active species (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract Requirements for cost and labor-effective quality control chemical analysis, which is friendly to environment and human health according to principles of green and white analytical chemistry, lead to challenges in instrumentations, their setup and testing methods. Low-cost and effective electrochemical detection platforms and procedures in flowing systems such as injection analysis, liquid chromatography, capillary electrophoresis, lab-on-a-chip and other devices have emerged as a simple and robust alternative to conventional tests. The technical report aims to address both current trends and future potential in this field for the development of new methods as well as fabrication and commercialization of the devices including miniaturization and portable assays realization under on-site, point-of-care, in-place, and field analyses. To achieve it, optimization and standardization of the flow-through systems and establishment of standardized testing protocols are of importance. Real application examples demonstrate the benefits of these systems developed with user-friendly interfaces for application in monitoring of biologically active species in biomedical diagnostics, and treatment, environmental protection, and food quality control.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".