HL7-Standard für semantische Interoperabilität: Was kommt nach HDF und SAEAF?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants have rapidly spread worldwide, causing coronavirus disease (COVID-19) with numerous infected cases and millions of deaths. Therefore, developing approaches to fight against COVID-19 is currently the most priority goal of the scientific community. As a sustainable solution to stop the spread of the virus, a green dip-coating method is utilized in the current work to prepare antiviral Ag-based coatings to treat cotton and synthetic fabrics, which are the base materials used in personal protective equipment such as gloves and gowns. Characterization results indicate the successful deposition of silver (Ag) and stabilizers on the cotton and polypropylene fiber surface, forming Ag coatings. The deposition of Ag and stabilizers on cotton and etched polypropylene (EPP) fabrics is dissimilar due to fiber surface behavior. The obtained results of biological tests reveal the excellent antibacterial property of treated fabrics with large zones of bacterial inhibition. Importantly, these treated fabrics exhibit an exceptional antiviral activity toward human coronavirus OC43 (hCoV-OC43), whose infection could be eliminated up to 99.8% when it was brought in contact with these fabrics after only a few tens of minutes. Moreover, the biological activity of treated fabrics is well maintained after a long period of up to 40 days of post-treatment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it