Bibliographic record
Abstract
Brief Biosketch Professor Klarskov Klaus did his Post-doctorate (Senior Research Fellow) in 2001 and Post-doctorate (Research Fellow) in 1997, State University of Ghent (Gent) and Doctorate (Doctor of Philosophiae) in 1991, Odense University. Dr Klarskov is interested in oxidative-stress induced post-translational protein modifications and their consequences in pathologies like adverse-drug reactions, cardiovascular and neurological diseases. The professor is having about 65 peer-reviewed publications in his credit and many presentations in various conferences. Viral infections are continuously challenging humanity. Influenza is a common yearly threat to people with the deficient immune system. Recently, a new strain of coronavirus (covid-19) causing similar symptoms to influenza albeit with frequent severe respiratory illnesses, was first reported in Wuhan (China). This virus has spread to most of the world's populations. Although extensive measures have been established in many countries to control the spread of the virus, continuous outbreaks still occur. From an early time, plant extracts have been known for their beneficial effects to treat common viral infections. Polyphenols represent a large group of chemical compounds (antioxidants) present in plant extracts. In this presentation, scientific examples that demonstrate the potential beneficial effects of polyphenols to inhibit viral (influenza and corona) proliferation in vitro will be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".