Capillary Electrophoresis Technique for Metal Species Determination: A Review
Bibliographic record
Abstract
Jarmila Harvanováab* & Lynda Blooma a Analytical Solutions Limited , Toronto , Ontario , Canada b Department of Chemistry, Biochemistry, and Biophysics , The University of Veterinary Medicine and Pharmacy in Košice , Komenského , Košice , Slovakia * Department of Chemistry, Biochemistry, and Biophysics, The University of Veterinary Medicine and Pharmacy in Košice, Komenského 73 , 041 81 Košice , Slovakia E-mail: harvaja@hotmail.com Metal speciation has increasing applications with regard to environmental chemistry, toxicology, and the biomedical sciences. Much of this interest arises from a growing need to identify the metal species responsible for toxic effects on environmental and biological systems. Previous studies have shown that the mobility, bioavailability, and toxicological properties of metals are all very dependent on the chemical forms in which they occur in the nature.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".