Determination of viloxazine by differential pulse voltammetry with boron-doped diamond electrode
Bibliographic record
Abstract
In recent years, a significant increase in the consumption of various pharmaceuticals, including antidepressants, can be noticed. Depression is classified as chronic or recurrent mood disorder that affects about 121 million people all around the world [ 1 ]. The continuous increase in the consumption of antidepressants has been recorded in most of the well-developed countries. The Eurobarometer report from 2010 indicates that almost 7.5% of the European population use antidepressants regularly [ 2 ]. Such a large intake of antidepressants is also associated with increasing concentration of these drugs and their metabolites in the environment. Many of them are not effectively removed by wastewater treatment, thus they are getting into sources of drinking water, groundwater or bottom sediments [ 3 , 4 , 5 , 6 , 7 ]. World Health Organization in report from 2012 suggests that concentration of pharmaceuticals in environmental waters usually did not exceed 0.1 µg dm −3 [ 8 ]. However, numerous research conducted in different countries such as USA, Canada, China, or Denmark indicate the occurrence of much higher concentrations of antidepressants in surface waters, reaching even 10 µg dm −3 , what is equivalent to approximately 50 nmol dm −3 [ 5 , 9 , 10 , 11 , 12 ]. These observations prove that monitoring the presence of pharmaceutical contaminants in the environmental waters is extremely important. Thus, it is essential to develop analytical methods that allow the determination of antidepressants in both biological and environmental samples with high precision and accuracy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".